Author: Ivana

The latest World Economic Forum’s Future of Work Report notes that humans and machines will spend the same time on business tasks by the end of 2025. This means that big data, machine learning, and artificial intelligence technologies will take the center stage in enterprise operations. Among the professionals who will be instrumental in realizing this future are data scientists.

But what are the skills for data science, and how much will they be in demand? Well, the Bureau of Labor Statistics estimates that careers in data science will grow two-fold between now and 2029. Typically, data scientists leverage mathematics, statistics, software, and programming skills to gather, analyze, infer, and draw customer insights from data. That said, here is a complete guide on the 2022 data science skills checklist.

What Are the Skills for Data Science Professionals?

Employers often look into certain skills and capabilities when hiring professionals to uncover information and insights from big data. Data science skills sets are segmented into technical and soft capabilities, which when combined enable data scientists to bridge the gap with all business stakeholders and achieve desired outcomes. Here is a close look into data scientist required skills.

Technical Data Science Skills

Some of the most important data science skills that employers consider before hiring candidates include:

AI/Machine Learning
Organizations are moving away from traditional AI/machine learning algorithms, originally termed “black boxes”, thanks to their ambiguity in deriving predictions, based on respective inputs. Current job seekers in the data science field should be well-versed with explainable machine learning technologies, such as SHAP and LIME. These innovative techniques help data scientists to create explanatory models with better logical communication for predictive options. Other algorithms that you should understand in this file include Naïve Bayes classifiers, decision trees, and random forests.

Statistical Analysis
A typical day for a data scientist involves applying multiple statistical techniques and concepts to big data environments. With that in mind, it will help if you’re good at statistical analysis and its application to data science, such as probability, variance, distribution curves, as well as standard deviation. Having a technical background in these concepts will help you to gather, aggregate, analyze, and interpret data, to present findings to business stakeholders. In other words, you need statistical analysis skills to help your potential employer draw insights from raw data.

Understanding of SAS and Other Analytical Tools
Employers require data scientists to have a strong, technical background in Statistical Analysis System (SAS) for business intelligence, and other associated tools for exploring and drawing business insights from large data sets. The adoption of SAS analytics is also increasingly becoming popular in machine learning and data science. Besides using SAS to create easy and insightful analytics, professionals in data science should also be able to integrate the solution with other basic business tools, such as Excel and Outlook.

Software Engineering
Aspiring data scientists should have software engineering skills, especially if they are going to build and deliver multiple machine learning modules in the backend. All this typically happens in full-stack development, which requires in-depth proficiency in Python, R, Java, JavaScript, and Scala programming languages. Other software engineering skills for data scientists are DevOps and MLOps. DevOps and MLOps are both software development strategies that focus on collaborations between developers, operations, and data science. While DevOps focuses on application development, MLOps focuses on Machine Learning.

Big Data
Big data, the backbone of data science, is arguably one of the fastest growing sectors globally, now standing at $274.3 billion. This is one of the most in demand data science skills. Job candidates in data science-related fields should understand the basic concepts of big data, types of data in big data environments, as well their applications. These skills should extend to the 5 Vs of big data, including volume, value, variety, velocity, and veracity. Lastly, a data scientist should understand the 5 A’s of big data, such as agility, accuracy, adoption, automation, and accessibility.

Writing SQL Queries & Building Data Pipelines
Although data scientists don’t take fundamental classes on programming and coding, they learn these skills out of necessity, to perform their roles in big data environments, such as writing SQL Queries and building data pipelines. A recent survey by Kaggle, a subsidiary of Google reveals that nearly 80% of data scientists use Python programming language, while another 40% use SQL in their current job positions. All the same, prevalent programming skills for data science that most employers look at include Java, C, C++, and Julia.

Data Analysis
Another thing that shouldn’t miss in your data science skills checklist includes data analysis, which will help you extract data from secondary and primary sources, before reorganizing the same in easy-to-read formats. That means having an in-depth understanding of Structured Query Language (SQL) to process or compute voluminous data sets. You’ll also need hands-on skills in spreadsheet presentations, given that your employer might need summarized spreadsheet reports.

Data Visualization
Data scientists are involved in the whole process of data analysis, from the top to the bottom, including presentation, which requires strong data visualization skills. This proficiency extends to knowledge in prevalent tools, such as D3.js or Tableau that can help you create various types of data visualizations, such as:

• Scatter plots
• Pie charts
• Bubble charts
• Line and bar graphs
• Heat maps
• Histograms

Hands-on skills in these types of data visualization as well as the respective tools for creating them will help you communicate business-ready insights in a detailed, meticulous storytelling approach for in-depth comprehension.

Data Wrangling / Feature Engineering
One of the top technical data science skills in 2022 is feature engineering or data wrangling. Typically, having data wrangling skills means you can transform one data set format to another as you build models, explore feature upgrades, or perform deep dives. At the same time, you should be able to extract features from raw data using your feature engineering skills.

Natural Language Processing
Technical proficiency in natural language processing, popularly abbreviated as NLP is a must-have skill if you want to be employed as a data scientist in any industry. As an extension of artificial intelligence (AI), NLP is increasingly becoming popular in the race to business automation. Organizations are already employing their use cases in building chatbots and virtual assistants, as well as monitoring consumer engagement on social media platforms. As a data scientist, you’ll also use NLP to run sentimental analyses of target audiences or extract specific texts from big data.

Deep Learning
A majority of data scientists work with AI and Machine Learning technologies. However, there is a rising trend in employment, where these professionals are hired to execute machine learning applications using advanced methods, such as deep learning. Data science skills to learn in Deep Learning include neural network application in creating advanced analytical models. Typically, you’ll need hands-on skills in various algorithms, such as

• Convolutional Neural Networks (CNNs)
• Long Short Term Memory Networks (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptrons (MLPs)
• Self Organizing Maps (SOMs)
• Deep Belief Networks (DBNs)
• Restricted Boltzmann Machines (RBMs)
• Auto-encoders

Soft Skills Needed for Data Science

As noted earlier, there are various data science soft skills that can enhance your chances of getting employed, including:

Data Intuition
Data intuition is probably the most critical among all soft data scientist skills, especially when it comes to big data environments. A good professional in this field should have innate intuition that enables them to look beyond apparent reports of data analyses to draw valuable business insights from them. However, acquiring this soft skill should worry you that much if you are a junior data scientist as it comes with experience and exposure to different big data projects.

Great Communication
It goes without saying that an ideal candidate for any job position should have strong written and oral communication skills, data science positions not being an exception. Your skills should extend beyond gathering and extraction of data to great communication skills which will enable employers to gain from your services. Moreover, mastering good communication skills means a successful career as you can always communicate your thoughts and ideas without shying off, something that can propel you to senior data science positions.

Proficient at Working with Unstructured Data
Working with unstructured data can be pretty much challenging, as opposed to structured data. That’s why employers look for skills beyond technical proficiency in unstructured data tools. For instance, a data scientist who exhibits patience can wait for relatively long periods to detect new and changed data. At the same time, patience comes in handy because you have to manage and analyze unstructured data manually, as well as derive valuable insights from it.

Developing Metrics
A good data scientist should be able to measure results against the effort put into work. That means having soft skills in metric development, which essentially translates to creating a metric, writing it in code, and automating a pipeline for periodic monitoring. Most importantly, you should be creative in coming up with metrics that other members of the organization can easily follow and relate to. On top of that, a data scientist should know how to calculate and present metric findings to business stakeholders.

How to Improve Your Data Science Skills & Learning Resources

Data science can be a lucrative and fulfilling career in the long term if you know how to make yourself indispensable in your new organization. Among the tips that most professionals employ is improving their skills. With that in mind, here is how to improve data science skills through online courses and self-learning websites.

Online Data Science Courses

One of the best places to learn or improve your data science skills is online, where several courses are available for both beginners and ongoing. For instance, you can improve your skills by registering at:

• Harvard University Data Science Certificate: Covers various data science fields and awards credits in at least 4 certificate courses, including data sampling, data analysis, data prediction, data management, and communication.
• Code with Google – Applied Computing Series: Offers an advanced Machine Learning Crash Course through video tutorials, hands-on exercises, interactive sessions, and case studies.
• California Institute of Technology Learning From Data Course: Caltech’s professor Yaser Abu-Mostafa can give you video course instructions on data science algorithms, as well as features a Q&A.
• Master of Information and Data Science (MIDS) at UC Berkeley School of Information: Offers live class courses for ongoing data science professionals to hone their skills when it comes to solving problems using complex data.
• Udacity: Offers online lessons that teach in-demand tech skills to help practising data science professionals be ready for future jobs.
• Coursera: Offers industry-recognised Azure Data Scientist Associate Certificate, which is ideal for both experienced and entry-level data analytics professionals.
• Cloud Academy: Teaches practical data science skills with a special Python course. Users can access the digital training tools remotely.
• Udemy Courses: The platform matches data science students with global tutors for tailored courses. Alternatively, learners can purchase tutorial videos that are published every month.

Online Data Science Tutorials

Innovative data scientists should always learn on their own to improve and stay on top of emerging big data trends. Prevalent data science tutorials that can facilitate this include:

• Codementor: Offers beginner and professional tutorials on data analysis, an introduction to machine learning, and how to choose the right software package for data analytics.
• Topcoder: This source gives you access to industry experts for career insights, as well as video tutorials that teach the basics of data science concepts. It can be a good place for networking too.
• KDnuggets: Offers beginner tutorials on the whole concept of data science, from gathering, sorting, analyzing, and interpreting, to visual presentation.
• Flowingdata: Dr. Nathan Yau, Ph.D., prepares course tutorials that help data science professionals to improve their presentation, analysis, and understanding of data with regard to current trends and real-life scenarios.

Other Resources for Improving Your Data Science Skills

• The Open Source Data Science Masters: You can sign up on this resource to access fats science tutorials, books, or even online study groups to improve your current skills.
• Learn Data Science by nborwankar: Offers data science tutorials through IPython Notebooks that cover linear regression, data analyses, and random forests, among other data science fields.
• Data Science Weekly: Data science professionals can sign up for weekly newsletters from this resource to get updated on the latest job listings, trends, and industry news.
• FiveThirtyEight: Data scientists who want to specialize in politics, economics, health, or sports niches can check this resource for relevant podcasts and insights from industry experts.
• Simply Statistics: This resource offers a heap of articles authored by Jeff Leek, Rafa Irizarry, and Roger Peng, to help data scientists improve their skills.
• International Conference on Machine Learning: The conference supports budding professionals in machine learning to increase the tech’s awareness and support its application in various fields of data science.
• Reddit: This platform offers a well-connected online community of data scientists and related experts who share valuable insights to help each other solve complex problems using data.

Summing Up

Data is arguably the new digital gold, taking over key business operations at a faster rate than you can imagine. A profession that touches on business data, such as data science has a promising future, as far as modern enterprise applications are concerned. Use this guide to know the data scientist required skills in your respective field to get a certification or improve your proficiency. You can also check our blog section for insightful articles on data science and engineering professions.

Today, 80% of businesses plan to automate work processes within five years and consequently now more than ever, there is a huge demand for software developers. In fact, the surge has already started. A huge leap in active software developers occurred worldwide from 18.2 million to 24.3 million between 2013 and 2021 —and the number is predicted to reach 45 million by 2030!

However, while programming is an essential skill for anyone looking to start a career in tech, choosing the right language to learn is not easy. There are hundreds of options out there, and more are being created every day. So, how do you know the best programming languages to learn in 2023?

The answer, of course, depends on what you want to use the programming language for. Different languages are better suited for different tasks. Some languages are more popular than others, and some languages are more in-demand by employers.

In this article, we will take a look at the top 10 programming languages for 2023. These languages are selected based on their popularity, usefulness, and ease of learning.

What Is a Programming Language?

A programming language is a system of notation for writing computer programs. It is used to create a set of instructions that a computer can understand while performing tasks.

There are dozens of popular programming languages, and each one has different syntax, or rules, that dictate how the instructions are written. Which means some languages are easier to learn than others, and some are great for particular tasks compared to others. For example, some languages are better suited for building websites, while others are better for creating apps or working with data.

Programming languages come in two variants – statically typing and dynamically typing.

Static typing is a style of typing in which variables are given a specific type when they are declared and are known at compile time. Dynamic typing, on the other hand, is a style of typing in which variables are not given a specific type, but instead are checked during run time.

Both static and dynamic typing have their advantages and disadvantages. Static typing can help to avoid errors by catching type errors at compile time, while dynamic typing can be more flexible and easier to use.

In subsequent sections, we’ll take a closer look at a few of the common choices – grouping them by typing – and help you decide the best programming language to learn in 2023.

So, without further ado, let’s jump right in!

Statically Typed Languages

Go

Go, also known as Golang is an open-source programming language that was created by Google in 2007. It has been used for many different purposes and is now used by multiple big companies including Google, Dropbox, Twitch, and Uber. With between 1.2 to 2.7 million Golang developers, it’s easy to see why it’s one of the most popular languages out there.

Learning Level: Beginner to Intermediate

Skills Needed: Golang can be learned by someone who already knows one or several programming languages, particularly Python, PHP, and C++.

Pros:

• Easy to use and simple to understand
• Rich in features and functionalities
• Built-in web server & a standard library
• Adaptable to C programming
• The programme can be modified and executed instantly
• Backed by Google

Cons:

• Its generic features frustrate many developers
• Library support is very limited
• Is not versatile
• Lack of GUI library

Platform: Cross-platform (desktop)

Popularity: Increasingly becoming popular, especially among data scientists.

Average Annual Salary: $164,824 / yr

You can check out this Getting Started with Go specialization course to see if this language fits you.

Kotlin

Kotlin is another best programming language to learn in 2023. It’s easy to learn and can be used for Android development as well as backend services. It was designed by JetBrains in 2011 and has been well received by Java developers who want an alternative but still want the familiarity of Java syntax/style.

Companies using Kotlin include Pinterest and Coursera.

Learning Level: Beginner to Intermediate

Skills Needed: Knowledge of programming languages, especially Java

Pros:

• User-friendly
• Great Java interoperability
• Clean and intuitive syntax
• Supported by both Google and JetBrains

Cons:

• Compilation speed varies
• A low developer talent pool
• Learning resources and tutorials are limited compared to Java

Platform: Desktop, Mobile, Web, Server.

Popularity: Increasingly popular among Android application developers.

Average Annual Salary: $132,558 / yr.

Check out this Kotlin for Java Developers course to see if you are interested in Kotlin.

C#

C# is a general-purpose, object-oriented programming language developed by Microsoft and first released in 2000. C# combines the syntax of C++ with object-oriented features that allow you to write code that runs faster than other languages with similar functionality. This makes C# ideal for games, web apps and mobile apps.

Learning Level: Intermediate

Skills Needed: Basic background in coding.

Pros:

• Object-oriented programming
• A high-level language that can access memory
• A broad range of .NET capabilities
• An integrated development environment (IDE)
• Inbuilt garbage collector

Cons:

• There are some limitations to C#’s performance
• Learning C# isn’t easy
• .NET platform dependency
• X-Platform GUI is poor

Platform: Cross-platform, including mobile and enterprise applications.

Popularity: It is considered one of the most popular programming languages, following Python and C++.

Average Annual Salary: $108.256 / yr.

If you are interested in learning C#, this Comprehensive approach to C# Fundamentals is the best place to get started.

Swift

Swift is a programming language developed by Apple that runs on macOS and Linux. It was originally released in 2014.

Swift is a general-purpose programming language that runs on the LLVM compiler infrastructure. It has a reputation for being fast and efficient. It has a small but dedicated community, and it has become a powerful tool for writing apps for iOS, macOS, Windows and Linux.

Learning Level: Beginner to Intermediate.

Skills Needed: Zero coding skills are necessary.

Pros:

• Easy to learn
• An organized syntax
• There is less code
• Faster than similar programming languages
• Source code is open

Cons:

• The language is still young
• Third-party tool interoperability is poor
• Does not support iOS older versions

Platform: Mobile (Especially iOS)

Popularity: Swift is the 15th most popular programming language on TIOBE, and it is widely used to develop iOS apps for the iPhone, iPad, and Apple Watch.

Average Annual Salary: 96,856 / yr.

If you are interested in learning Swift, check out this course on Introduction To Swift Programming.

Typescript

Typescript (or TS) is an open-source typed functional programming language developed by Microsoft in 2012. It was originally proposed as a superset of JavaScript but has since evolved into a fully-fledged modern language with static typing, class inheritance, generics and more.

Typescript offers better support for functional programming and static typing (like Java) along with many other features like type checking and error-prone syntax removal.

According to the 2021 State of JavaScript Survey, TypeScript is the “most adopted technology for that year.”

Learning Level: Intermediate to Advanced

Skills Needed: Requires some familiarity with JavaScript.

Pros:

• Rich IDE support
• Early bugs spotting
• Quick refactoring
• Object-oriented programming (OOP)
• Cross-platform and cross-browser compatibility

Cons:

• You must first be familiar with JavaScript.
• Code can be bloated

Platform: Web

Popularity: According to the 2019 Developer Survey by Stack Overflow, Typescript is one of the most popular programming languages with a 73.1% acceptance.

Average Annual Salary: $71,299 / yr.

If you think that Typescript is the right programming language to learn in 2022, get started with this Introduction to TypeScript course on Udemy.

Scala

Scala combines the finest features of functional and OOP programming languages into a single, unique high language.

Scala has fully supported object-oriented programming since its inception and has been used for developing high-performance software. This makes Scala one of the most preferred languages for the development of large-scale applications and it is also integrated with Java, which makes it easy for developers to develop complex applications using both languages.

Netflix, Twitter, and the New York Times are among the businesses that use Scala.

Learning Level: Experienced.

Skills Needed: Java, Python, C, and C++ skills needed.

Pros:

• Faster than Python
• The best for data analytics
• Excellent performance

Cons:

• It has a small community presence.
• There isn’t much backward compatibility available in Scala.
• The complexity of Scala’s features and concepts makes learning more difficult.

Platform: Cross-platform.

Popularity: Scala’s popularity has suffered as a result of its complexity. It is presently ranked 26th on the TIOBE 2022 index.

Average Annual Salary: $149,152 / yr.

To see if Scala is the right fit for your career goals, check out this Effective Programming in Scala course to learn more about the language.

Java

Java is a very popular programming language that has been around for decades so it’s no surprise it’s one of the most popular languages on this list.

Java is used for many different types of applications, including web applications and mobile apps. It’s also very easy to pick up if you’ve never programmed before.

Learning Level: Beginner Level (Java is considered to be quite easy for beginners to learn).

Skills Needed: Servlets and JavaServer pages, Scripting languages like HTML, CSS, and JQuery, Markup languages like XML and JSON, Web frameworks.

Pros:

• Automatic Garbage Collection
• Object-Oriented Programming Language
• Java is Simple to learn
• Maintenance is inexpensive and economical
• Portability feature
• Security is a key feature of Java
• Supports multithreading
• Platform-independent

Cons:

• Poor performance and slow response time
• GUI needs improvement
• Backups are not available
• Requires a significant amount of memory
• Extensive and complex code
• Not free

Platform: Cross-platform (Windows, macOS, Linux, and Unix operating systems)

Popularity: Java remains one of the most popular and widely used programming languages in the world. According to the TIOBE index for November 2022, it is the third top programming language for developers.

Average Annual Salary: $99,713 / yr.

If you are looking to learn Java in 2023, a good place to start is this Introduction to Java course offered by Learn Quest on Coursera.

Rust

Rust is a systems programming language that offers speed. It’s designed to be fast, safe, reliable, and long-lived. It also has a strong focus on safety and concurrency thanks to its “zero cost abstractions,” which means that It can help you write programs with excellent performance profiles fairly quickly.

Rust targets systems programming where other languages like C or C++ raise problems with memory errors and concurrent programming.

Learning Level: Intermediate/ advanced level.

Skills Needed: Basic experience in system programming.

Pros:

• Ensures memory safety while delivering high performance.
• Concurrent programming is supported.
• Has a vibrant development community
• Ensures backward compatibility and stability.

Cons:

• It does not have an efficient garbage collection system.
• A bit slower than Ruby and Python.
• It has a steep learning curve.

Platform: Cross-platform (Windows, macOS, Linux, and Unix operating systems)

Popularity: Rust is a very popular language. On Stack Overflow, it has remained the number most loved language followed by Python.

Average Annual Salary: $129,484 / yr.

If you are interested in a career as a rust developer, then you may want to check out this Rust Programming For Beginners course on Udemy.

Dynamically Typed Languages

PHP

PHP is a server-side scripting language designed for web development with a strong focus on object-oriented programming (OOP).

PHP was originally created by Rasmus Lerdorf in 1994, who released it in June 1995. It has evolved over time but still remains popular today due to its popularity in web applications and other web services (like WordPress). It also offers great performance because it was designed from the ground up with speed in mind.

If you are thinking of the top 10 programming languages in 2023, PHP must be top on the list.

Learning Level: Beginner to Intermediate

Skills Needed: Basic background in programming.

Pros:

• Free and open source
• Independent of platforms
• An excellent library support system
• Easy-to-use
• There is no need to write lengthy code

Cons:

• It is not suitable for building large web applications
• Does not allow for modification or change
• Not much in terms of security
• Error handling is poor

Platform: Desktop, Mobile, Web.

Popularity: Very popular. Powers 77%+ of websites.

Average Annual Salary: $98,395 / yr.

Check out this PHP course if you want to know how to build web applications in PHP.

Ruby

If you are looking for a career as a programmer, then you should learn Ruby. It is one of the top 10 programming languages in 2023.

Ruby is a server-side scripting language that was designed and developed in the mid-1990s by Yukihiro “Matz” Matsumoto. Ruby allows developers to build software in an easy way and uses a simple syntax. It is used in many web applications including Rails, Sinatra and Merb.

Ruby on Rails is a web application framework written in Ruby that helps you build better web applications faster. Some of the popular companies using Ruby include Airbnb, Groupon, GitHub, and Shopify.

Learning Level: Beginner Level

Skills Needed: Basic coding proficiency in languages like CSS, HTML, and JavaScript and some experience in web development.

Pros:

• A robust community of ruby developers
• There is less code to write
• Robust library
• Enhanced readability of code.
• This is an easier language to learn.

Cons:

• Performance issues
• Flexibility issues
• Inadequate documentation

Platform: Web

Popularity: Ruby has been ranked by W3Techs as 5.6% of websites whose server-side programming languages are known.

Average Annual Salary: $129,532 / yr.

Want to learn more about Ruby? Check out this Introduction to Ruby course.

R

R is an open-source programming language that was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand in 1993 as a means to make statistical computing easier.

R has grown into one of the most popular languages for statistical analysis, data science and machine learning today. R makes it easy for developers to create complex statistical models using just a few lines of code.

Learning Level: Intermediate Level.

Skills Needed: Javascript, Python, and Math skills.

Pros:

• Open Source
• Adaptable to any platform
• Machine Learning Operations
• Exceptional data manipulation support
• Constantly growing

Cons:

• It is difficult to learn
• R lacks basic security measures
• In comparison to Python or MATLAB, R is slower
• It consumes a lot of memory
• Poor documentation

Platform: Cross-platform (Windows, macOS, Linux, and Unix operating systems)

Popularity: R is the 12th most popular programming language as of October 2022, according to the TIOBE index.

Average Annual Salary: $77,817 / yr.

Check out this Introduction to R Programming for Data Science course to see if R programming is a good fit for your career goals.

JavaScript

The Javascript programming language is another one that’s growing more and more popular every year because it’s an extremely flexible tool that can be used in many different contexts. It’s also easy to learn because of its simple syntax structure. If you know any HTML or CSS then you’ll be able to figure out some basic JavaScript code right away!

Learning Level: Beginner to Intermediate Level.

Skills Needed: Knowledge of HTML and CSS.

Pros:

• Innately fast
• Versatile
• Easy to understand and learn
• Good compatibility with various programming languages Reduced server workload

Cons:

• Client-side security
• Poor Support for older browsers
• Lack of debugging facility
• Only supports single inheritance

Platform: Cross-platform.

Popularity: Javascript is definitely the most popular programming language in the world. In fact, javascript powers about 97.4% of all the websites in the world.

Average Annual Salary: $96,051.

If you are interested in learning Javascript, check out this JavaScript for Beginners Specialization.

Python

Python is the best programming language to learn in 2023 if you’re interested in web and software development. It is a general-purpose language and can be used to create different programs for different problems. It has a huge community of programmers and can be used by developers and non-developers.

Learning Level: Beginner to Intermediate Level

Skills Needed: The basic understanding of programming.

Pros:

• Python is simple to learn and understand.
• It increases efficiency.
• There are several libraries and resources available for Python.
• It is adaptable.
• Python has an active community.
• It is free and open-source.

Cons:

• Python has a speed limitation.
• Runtime mistakes are possible with Python.
• Python uses a lot of memory.
• It is not very suitable for mobile computing.

Platform: Cross-platform.

Popularity: Python programming language is the most popular and most commonly used programming language in the world. According to the TIOBE index for November 2022, it is the number one top programming language for developers.

Average Annual Salary: $102,528 / yr.

If you are interested in learning Python, check out this Python for Beginners course on Udemy.

Wrapping Up

It’s never too late to learn a new programming language. In fact, there are many reasons why learning a new language can be beneficial, including the potential to give your career a boost. This article provided a list of some of the best programming languages to learn in 2023.

So whether you’re looking to boost your career or simply want to try something new, these choices are a great place to start.

Happy coding!

FAQs on Programming Languages to Learn

Sports betting operators worldwide are exploring the potential of mobile applications, considering the rapid growth of both the gambling industry and the use of mobile devices. According to market research conducted by Technavio, the sports betting industry is expected to grow by USD 134.06 billion in 2020-2025, with significant growth of 39% in the APAC region with China and Australia as the key players in the markets. This makes for tough competition, so naturally betting operators will be focused on differentiating themselves from their competitors.

A significant influencer on success are the sports betting app features that directly impact the overall user experience of the gaming app. This starts with the most basic functionality such as the ability to create your personal account in order to place bets and ti withdraw your winnings, to far more advanced features such as integration with VR and wearable devices or the creation of a social betting app.

In this post, we shall look over what all these apps have got to offer, from your regular sports fan or to a seasoned bettor.

Top Features to Нave in a Sports Betting App

Modern advances in technology make it possible to “equip” your sports betting application with a wide range of features to cater to a bettor’s needs and enhance their user experience. There what we might term a main, or core set of features that are a must-have for any kind of application, some will be specific for the gambling industry, and then there are best in class, highly innovative technologies that are just making their way into the industry. Depending on what tech solutions the and integrations the business stakeholders developers choose to implement in the application, they can create a sports betting app that will attract players, engage them in betting, and deliver an excellent user experiences. Innovations present an exciting opportunity for them to create something unprecedented and revolutionary.

One should also keep in mind that certain app features are dictated by the specifics of the gambling industry. Betting operators have to adhere to strict regulations in regulated jurisdictions and take into account local legislation if they want to bring their app to the global market. Building sports betting apps is not an easy task but do it well and the rewards are significant. Thus, you want to deliver an app that is not only excellent for the user, but remains compliant.

Let’s take a look at some of the must-have sports betting platform features to consider introducing into your application:

• Login and registration can be available with email and password, or with a social media account. Different options apply to regular users and admins. For instance, creating user accounts may be age restricted and depend on location, if the user is trying to access your app from a country that you are not licensed to operate in or prohibits gambling altogether.

• User Onboarding / Betting Guide for the user is a great way to introduce a new player to the app, show them around and explain how to place bets, what functionalities are available, etc. A starter guide can be implemented as interactive pop-up instructions that the user encounters when first opening the app, setting up the account specifications, or placing the first bet. Furthermore, a betting guide page would be helpful for players that are new to betting and gambling to get them familiar with how it works.

• Personalized dashboard for the user is what your user sees every time they open up the app. Players want to have the features that they use most often right at hand. Also, the dashboard is where you place personalized recommendations to cater to the user’s likes or encourage them to explore something new that will be to their taste. This was the case with Graphyte when they introduced a smart lobby with personalized product recommendations. All this is made possible with AI/ML integration that allows to collect data on user behaviour in the app and analyse patterns which can be later used to generate personalized recommendations. At today’s level of technology, AI content personalization can be done seamlessly and help create a genuine experience for the user.
• Admin Panel or a separate dashboards should be set up for the admins, as they need to use functionalities that differ from that of a user dashboard. Admins need access to user management – access to user accounts, option to add or ban users from the game, view transaction history; sending notifications, content management for adding new events, user and game statistics.
• Creating a wager should be simple and intuitive enough to let the user do it without hesitation. Allow the user to set their own picks and bet amounts.

• Search with a filter option for wagers allows your users to easily filter out wagers by date, status, sports, players and their average betting rates, etc. What’s more, app developers can leverage AI technologies to create a powerful search engine with strong personalisation that is self-learning, basically putting it one step ahead of any other search option you can possibly offer to your user. AI-powered smart search helps your users find exactly what they need with pristine precision. A great example of this is Graphyte’s intelligent search engine. With AI technologies they built a search engine for betting and gaming sites that allows a bettor to look up events or game results in a fraction of a second. Search results are personalised to that particular user so that they get exactly what they need. And it being AI, the smart search engine only keeps getting better and self-calibrating itself to each individual user and their needs.
• Payment integration is an important feature to implement in sports betting app development since you want your users to be able to easily place their bets and withdraw winnings. This can be done in a few different ways, the first obvious one being enabling users to link their cards. However, some users may run into issues if they are from a location that places restrictions on online gambling. In 2020, the UK Gambling Commission decided to ban credit card transactions for gambling, and that same year one of Australia’s major banks came to the same decision, even though Australia boasts one of the biggest gambling markets worldwide.
  It’s good to offer more varied payment options for the user in case they are restricted to use one or the other. Consider such payment gateway integrations as Stripe, Netller, Skrill, Cardinity, and others. Bitcoin payment integration is also on the rise and as relevant as ever in the iGaming industry. Allow the user to add multiple payment options and let them link their accounts to their in-app wallet for easy one-click bets.
  The main thing is that the payment should be easy, but secure. As in the case with Ladbrokes Coral, they created an optimized and systematized payment solution which allowed them to subsequently cut costs and save revenue, as well as introduce additional features for the users.
• Real-time live scores help keep the users interested and encourage them to gamble more frequently. This is particularly relevant for sports betting, especially certain kinds of sports. For instance, this feature is often implemented in football betting apps.
• Notifications are a must to keep the user informed about the game outcomes, final ranks and match summaries. These can be in-app or push notifications. A good rule of thumb is to not go overboard with push notifications. Otherwise, instead of being a useful feature it may turn into a nuisance. If a piece of news is important for the bettor to know and is time sensitive, that’s when you know to use push notifications.

• Live Streaming of games and competitions is for the user’s convenience as they can place bets while following the game. If you are integrating live streaming in your app, consider utilizing cloud technologies, lest you want to spend a fortune on maintaining servers. Make sure to include match schedules and alerts for when the stream starts so that the player can get good use of this feature.
• Multi-language support becomes necessary once you have a growing audience in language-specific locations. This adds on to the personalized experience of the user and encourages the growth of that specific market share even more.
• Security is essential for betting platforms as the user discloses to the app personal and banking information in order to place bets or withdraw winnings. Security in iGaming is important to protect the user from harmful data leaks. To be resilient to cyberattacks and fraudulent activities, it’s crucial to build sports betting apps that are secure by design.
• Predictive analytics and deep player and team performance data along with player and team stat prediction integrated with sports betting allows the player to plan out their bets, see win probability and expected revenue depending on the collected and analyzed daіta. It’s a good way to generate more interest in using the app and placing bets. The bettor will be likely to enjoy the games and placing bets more as they become familiar with team and player insights, start recognizing trends, and make more informed decisions.

• Cross-platform compatibility is something to consider when you want to expand your market reach and also allow your users easy access on just about any platform. If some users have their preferred way to access a betting app, there is an intersection that represents bettors that don’t have a preference and switch between platforms and devices. These will be the most dedicated players that don’t limit themselves to only one outlet and are willing to place bets anywhere and anytime.
• Tracking statistics and results when offline would be useful for players who have low connectivity. This way, even if a player loses internet connection, they won’t miss out on checking game results and stats.

Some More Features to Consider

The next features are not necessarily ‘must-haves’, but you may as well top up your app with a few of these. Others may require a bit more effort to implement and take more time to hit the target. Still, it’s good to know your options.

• Social features are all kinds of functionality that is closely linked to social media platforms with their focus on providing a place for people to get in touch, share information and opinions. Users may follow one another, send private messages, comment on and share in-app news and posts to their social media accounts.
  Public chats or forums are an interesting way to start conversations and invite users to be more active in the app, making it a fully-fledged community. Apps that provide this functionality are called social betting apps, which are social media and betting all in one. Social betting gives a platform for engaged bettors to interact with peers, share tips and advice, and start conversations. These are large sports betting communities that exist within an app.
• Checking bet slip in app is targeted at bettors who prefer to place their wagers at a betting shop. This innovative feature allows customers to scan their shop bet slip and monitor bets in the app. This way they can enjoy placing bets in the way they are used to, yet still get to experience the convenience of using a betting app and following bets on the go.
• Immersive game experience with AR/VR is making rounds in the gaming and betting sector as it allows players to watch their favourite sports in virtual reality with 360-degree videos of games or training. This feature does not come as first on the list of our ‘must-haves’ because it does require the user to have a VR headset in order to get a fully immersed experience. However, with technology becoming more mainstream and available to just about anyone, there’s no saying when it will become an integral part of all apps for betting.
• Exclusive Odds is something that a bookmaker can offer as a way to persuade the bettors to opt for using their betting app to get that extra boost rather than going to the more familiar betting shops. This feature is more of use to them because they want to make a shift in the way they offer their services. However, relevant for now, it may quickly become redundant as betting shops are rapidly closing down and we are observing this transition to online gambling even without the need for that extra push. Moreover, new bettors that just discover the world of sports betting may start out as mobile betting app users from the get go.
• Bonuses and discounts are something you may consider as an extra feature to spice up the players interest although, as practice shows, this is now becoming obsolete for a couple of reasons – everyone offers bonuses and discounts so it’s not exactly the feature to put you above competition, and there are only so many free spins the player can use before it gets boring. Ultimately, your target audience is not as thirsty for freebies as they would be even five years ago. But if it doesn’t cost you an arm and a leg, you may just throw this one in there as well for good measure.
• IoT integration caters to those bettors who are active users of wearable devices, such as Apple Watch, and therefore are quick to jump on the opportunity to place bets no matter where, no matter when. Major bookmakers have already introduced such integrations into their apps. As the technology becomes more widespread and accessible, as long as you provide people the opportunity, placing bets with a simple tap on your smartwatch can be a regular part of a bettor’s experience, similarly to how they just as quickly got used to betting on their smartphones. It’s just another step forward in technological progress.
• Sports news is a good addition to your sports betting app. Your users may want to have access to all the relevant news about their favorite sports in the app. Although, some may consider it more of a “nice to have” feature and stick to strictly betting functionality. Sports news is a fun way to keep your player informed, and also it can be used alongside social features, e.g., commenting on news pieces.

Conclusion

Sports betting application development has its challenges and specific requirements that come with the industry. Nonetheless, bookies worldwide are swiftly moving forward with their shift to online applications to be used by bettors on smartphones or other devices, making sports betting as accessible as ever, all things considered. Development of betting and gambling apps requires impeccable technical skills, a real feel for the market, and a lot of dedication. Starting from providing all the must-have features to creating a seamless user experience with UI/UX design.

As was the case with Ladbrokes Coral, they developed a bold new user interface with optimized bet placement and new functionalities leveraging cloud native technologies – all of this allowed them to achieve record high customer satisfaction.

And keep in mind that developing apps for bookies is a multifaceted endeavour. The multitude of features you can offer to the bettor seems to be never-ending as it’s always topping up with new technological advancements. That’s where you need a development team of top experts with relevant experience in sports betting software development and a portfolio of industry-leader clients. When done wisely, you not only deliver excellent betting experiences to your users, but potentially create an engaged and dedicated community around your app.

It’s amazing how Java remains among the top development languages and platforms for so long, and by now it’s probably older than some of the developers that are using it.  
In 2021 it’s still in the top 5 according to the StackOverflow Developer Survey 

All thanks to the Java technologies innovation. The platform is evolving to meet the needs of the community and the global industry trends. Java keeps up with the serverless architecture, data science, IoT and reactive programming. 

The Symphony Solutions Java team has analyzed and presented their selection of the latest Java technologies and worldwide software development trends that influence the platform. Let’s continue to inspect what is sure to hit the spot, what remains relevant and what are some of the entirely new predictions. 

Machine Learning with Eclipse Deeplearning4j 

Eclipse Deeplearning4j is gaining popularity in Java nowadays. It is a library for machine learning written in Java for use in the JVM environment with its first preview released in March 2020. 

Developed specifically to be used in the JVM environment, it is compatible with other JVM languages. Deeplearning4j is considered a high-level framework that is used for building neural networks. This means that the toolset empowers you to perform a wide range of tasks that have to do with clustering and classifying data, as well as data analysis for high level predictions (e.g., weather forecast, predictions for the stock market, etc.). 

Deeplearning4j is used in cybersecurity for detecting fraud or anomalies, which makes it invaluable for use in fintech, e-commerce, and other sectors that require enhanced security for sensitive data protection. It can be integrated with other ML platforms (RapidMiner, Weka, Prediction.io). Some interesting use cases include retraining and deploying models in JVM microservice environments, mobile devices, IoT, etc. 

New features in Java 14 and 15 

Java versions 14 and 15 have introduced new features that make for a more streamlined and clean code, making the life of Java developers worldwide easier, allowing them to focus on creating the most value without getting lost between lines of code or drowning in neverending documentation. 

Some of the new features that were introduced and should be on your checklist are the following: 

• Record is a new type of class introduced in Java 15 to reduce boilerplate code and make it easier to create immutable data objects. 
• Switch expressions have been established as a standard feature since Java 14, and are now able to return value and assign it to the corresponding variable.  
• NullPointerException is another Java 14 feature, which is a step-up in debugging, as it points out the null variable and the name of the call that threw the exception.  
• Hidden classes, introduced in Java 15, are a useful feature for developers who work with dynamic bytecode or JVM languages. 

In fact, any regular Java programmer could probably tell you about their pick-of-the-crop new features introduced in Java releases 12 through 17, as there surely is something to pick from with Oracle rolling out a new LTS release every two years, next to the quarterly updates for the previously introduced versions. 
Though the most used is still Java 8 version, the latest versions are starting to gain momentum in 2021. 

Java Concurrency 

Java concurrency is another popular technology in Java. Concurrency programming, means that multiple programs or applications can run simultaneously, which is supported through multithreading and synchronization. It’s a lightweight process that allows accessing data from different threads, that in Java constitute the path that is followed whenever a program is being executed. 

Threads are created by the Java Virtual Machine when you run the program, which may work with multiple threads simultaneously to run a Java application. Concurrency varies depending on other processes that run in the operating system – the number of threads that can run concurrently on a normal CPU depends on the number of processors in the system, whereas a high-volume application can run thousands of threads with GPU without interruption. 

Java concurrency helps improve the throughput of the program and optimize the process, as you can run parts of the program in parallel and reduce the time that is required to complete a time-consuming task faster without reduced efficiency. 

What’s coming to replace Spring Boot

 Spring Boot is still the most trending Java-based framework that is used for creating microservices. It’s gained its popularity due to a gradual learning curve and simplicity of integration with other Java frameworks.  

However, we are observing new frameworks rising to their popularity that may well become an alternative, that are more lightweight and perform on par or better than Spring Boot. 

Vert.x 

Vert.x is another lightweight alternative to Spring Boot from Eclipse foundation. It is used for building reactive applications that are resilient and highly scalable. Vert.x is based on an asynchronous and non-blocking execution model. It also supports the use of multiple languages. When comparing Vert.x to Spring Boot in one of our latest articles, the former is vastly outperforming in terms of speed of processing concurrent requests, CPU and memory usage. 

Azure is the cloud competitor to AWS 

We can now observe steady growth in the popularity of Microsoft Azure as a competitor to AWS. What does it have to do with Java trends, you may ask? 

Java is the programming language of choice when it comes to cloud computing, due to the ease of use and it being possible to run the same program on different platforms. This makes selecting the right cloud environment oftentimes a big concern for Java developers who want to get the most out of the cloud services provided. For now, AWS is the cloud provider of choice for the majority, although Azure is catching up and generating a fanbase of its own. In fact, Microsoft Azure is working closely with the Java community on providing resources and tools to Java developers who want to work with cloud-native applications. 

Java developers may choose Azure as their prefered cloud computing platform for some or all of the following reasons: 

• Windows integration. 
• Built-in analytics tools. 
• Strong PaaS solution. 
• Highly visual and easy to use UI (little to no coding required). 
• Reliable storage capabilities and secure data encryption. 
• Active directory library for Java. 
• Fully managed cloud infrastructure. 
• Continuous integration and deployment tools. 

Whatever is your reason for opting for Microsoft Azure, the main point is that you meet your objective. And as practice shows, Azure has a growing popularity among startups and independent software vendors, and it’s used by more than 95% of Fortune 500 companies. 

Java Technologies and Trends In Retrospect: 2018-2019 

Java 10 and Java 11 

Our 2018 list of the latest Java technologies started with, well, Java. It was around that time when Java switched to releasing new versions every six months instead of three years. Fast releases mean more innovation. They also mean fewer differences between versions and a more complicated Long-Term Support (LTS). That’s why Oracle decided to provide LTS once every six releases or three years. Unfortunately, though, this only concerns the commercial support contract from Oracle. Public updates and other releases will only be updated for six months – until the next version comes out. 

Java 9 was the first incremental release, and two more came out in 2018: Java 10 and Java 11. Java 10, released in March 2018, introduced the local variable type inference and a new garbage collector interface. These features were supposed to simplify writing Java applications. But Java technologies innovation didn’t stop there. Java 10 has an improved JVM which is going to make it easier to use Docker containers. Java 11, released in September 2018, has all the features mentioned above plus it’s the first LTS support version since Java 8. The next LTS version will be released in September 2021. 

Keep Сalm and Learn Kotlin 

Kotlin has every chance to become a programming marvel. According to the Stack Overflow Survey 2018, Kotlin was the second most loved language among developers. In 2021 the interest to this language is still high – it’s in the top 15 on the list of the most loved languages among developers though the interest is a bit decreased. The Symphony Solutions Java team has already given their credit to this programming language. 

The language was designed to be loved by Java developers. It’s 100% interoperable with Java code, so much so that classes written in Kotlin can be mixed up with classes in Java and the solution will work seamlessly. Seriously, any Java library can be used within Kotlin. And since a lot of gigantic enterprise systems and Android applications use Java, a new and lightweight Kotlin is a gift to Java developers. 

While you may think it’s just another Java Virtual Machine language, Kotlin was designed by JetBrains, a company that knows a thing or two about the perfect Java IDE. Developed with the gaps of other JVM languages in mind, Kotlin is an expressive language (read: less error-prone) that’s not as verbose as Java. Its data classes and list operations reduce boilerplate code, and Kotlin offers nullability-safe extension functions and higher-order functions all Android developers will appreciate. 

Though Kotlin has some interesting use cases, it’s the Android apps niche that can and probably will propel it. It’s now the first-level language for Android app development. Google’s appreciation for the language and the commitment to support Kotlin means a lot for its future growth and popularity. It will be interesting to see if Kotlin finds its place in enterprise applications development or not. 

Reactive Java 

Reactive programming is nothing new, but it’s become more relevant than ever. The volumes of data are skyrocketing; the computational power is increasing. Users demand software integrity, fast updates, and non-stop synchronization. Processing numerous events becomes a pain in the back(end) for Android. It’s time to switch to a programming paradigm where you define the source of data and the consumer of that data. 

RxJava, first released in 2013, is still being quite actively used to this day. It is the reactive extension (API for implementing reactive programming) for the JVM. When you connect the consumer to the source of data, RxJava pushes the data to the consumer. This library can help developers build asynchronous, concurrent and resilient Java applications. It also makes the code look less bulky and more elegant. 

From Microservices to Serverless 

One of the most significant global trends in software design from 2018 up to these days is the shift from microservices to serverless architecture, and the current Java technologies also couldn’t resist it. Let us refresh your memory. The microservices architecture means that the system is modular and all of its components can be changed or replaced independently without harming the system. Microservices remain a popular way of building complex systems that comprise independent components, and Java developers find their ways around overcoming any shortcomings microservices may present to get the most out of the microservice architecture in Java. Since serverless is still at times a luxury solution with the cost of development and maintenance, we are not taking microservices off the shelves just yet. 

The serverless architecture means that you can build and deploy the code without having to configure the underlying servers. But the best part about serverless computing is that it breaks modules down into parts smaller than microservices. This allows almost infinite scaling. Also, that level of modularity gives developers a chance to visualize complex processes going on in their software, so it’s much easier to improve performance, reduce bottlenecks and even make architectural decisions. To sum up, the serverless approach gives you granular control over the architecture of a solution and simple horizontal scalability. 

So, how do you go serverless with Java? Two words: AWS Lambda, a serverless computing platform. All you have to do is create an AWS Lambda Function and implement a predefined interface. 

AWS Serverless Java Container 

The Java wrapper known as aws-serverless-java-container  (version 1.7) makes working with AWS Lambda easier. A Serverless Java Container natively supports API Gateway proxy integration for Lambda with request and response mappings. You can also create custom model mappings using VTL. The AWS Gateway API starts each Lambda as a container and then translates API Gateway proxy events into the request format that’s accepted by the Lambda. 

However, containers have the limitations of a single request at a time. Secondly, the bulkier the application, the higher the latency. To avoid this, the code footprint should be as small as possible with a minimum of required dependencies. 

If you want something smarter, though, look up Service Bloks. They are cloud-native apps that combine the advantages of microservices with the scalability of serverless functions. A basic service block consists of a Service Core and Functions. A service core is a Spring Boot application, and it’s responsible for communication and dispatching events to serverless functions inside a block. 

Data Science with Java

You can use Java pretty much anywhere, and data processing is no exception. The fact that Java is a high-performance, general-purpose, compiled language just proves the point. No matter what popular framework you choose, Hadoop or Apache Spark, there’s a well-supported Java client or implementation to work with. Not to mention clients for all sorts of SQL and NoSQL databases. 

Thanks to a big community, there are many libraries for data processing and cloud computing as well as machine learning. Keeping your ETL production code, data processing, and machine learning in the same ecosystem is obviously a good idea. 

Of course, Java is not perfect. Because it is a wordy programming language, Java isn’t the best fit for R&D or statistical applications. That’s why data scientists choose dynamically typed languages like R and Python instead. Competing with R is especially hard since it was created for statistics and research. Despite that, Java is successfully being used in Data Science thanks to its ubiquity, performance and seamless integration of the data science code into an existing solution. 

Java Maximizes Its Effects on Scripting Languages 

Not every programmer is a pro in Java development, but Java’s effects on other languages are something many of them experience. Just think of the massive number of languages that are now executed on the Java Virtual Machine. Various widely-appreciated scripting languages were written using Java, created for or ported to the JVM. This allows them to make use of the rich Java ecosystem. 

Java developers sometimes choose scripting languages as a quick and easy solution for tasks like prototyping, algorithms and mathematics. They are especially helpful when you need to perform a complex task in a minimal number of steps. 

Think of Groovy, JRuby and Jython. These popular and tightly Java-integrated scripting languages can help developers incorporate scripts originally written in Ruby or Python with Java libraries. You can get extra info and some JRuby inspiration by reading success stories on GitHub. 

Java has also become one step closer to scripting languages with the introduction of Java Specification Request (JSR) 223. Along with Java SE 6, which includes built-in support for script engines, it provides a standard interface and API to interact with dynamic languages running on the Java platform. 

Java Development Propels IoT to the Productive Future 

Another niche where Java leads the pack is IoT architecture environment. According to the IoT Developer Survey by the Eclipse Foundation, Java is the favorite language for building IoT solutions. And the future Java trends predict the same, due to its interoperability and portability. All thanks to JVM, as it lets developers write code locally and then move it to small pieces of hardware like chips. Java is one of the best choices for cloud and gateway development from the entire IoT infrastructure. Isn’t it convenient to use the same ecosystem for data measurement, collection and analysis? 

At the beginning of 2019, Oracle also updated the Java Card platform which provides security for smart cards and other limited-memory device applications. Version 3.1 of the platform offers increased support for IoT devices. 

Summing Up 

There are numerous reasons why Java keeps landing among the top technologies for business and web. It’s the combination of simplicity and robustness, the popularity of Android and web applications, and, of course, an enormous, friendly community. 10 million developers can’t be wrong. 

Although no fundamentally new Java technologies are going to be delivered anytime soon, the platform is flourishing and has no intention to stop. The latest Java technologies follow global trends in software design and development. This includes reactive development, serverless architecture, and data science. The rise of the JVM languages, the breakthrough of Kotlin, and the perfect match of Java and IoT infrastructure prove that Java has no intention of stopping in future. 

You may have noticed that some of the Java trends overlap with the previous years’ tendencies. That’s no surprise since the Java platform manages to balance beautifully between stability and innovation, and continues its influence on the rest of the programming world. 

At Symphony Solutions, we care about Java and are always eager to discuss modern Java technology. Check our open Java job positions and do not hesitate to apply! 

Oracle rolled out Java 17, the next long-term support release of Java in September 2021. Today we will focus on the Java new release features, which can be used in the daily development lifecycle. 
All features are generally available and enabled by default, except if they are labeled with one of the following: 
Preview – features are fully specified and implemented, but not yet considered to be final. They are considered to be almost complete, waiting for an additional round of real-world feedback. These features have to be explicitly enabled. 
Experimental – features are less stable and more likely to change. They also have to be explicitly enabled. 
Incubator – modules are non-final tools and APIs, and are distributed in separate modules. 
All the features I’ve singled out have been officially added to Java and are past their preview phases. 

Let’s take a look at 14 essential features from Java versions 12 to 17. 

Java 12 features 

1. Java 12: File mismatch() method 

The method mismatch(Path, Path) compares two specified files and returns the index of the first byte where they differ or -1 if they don’t. 
The return value will be in the inclusive range of 0L up to the byte size of the smaller file or -1L if the files are identical. 
File relationships and their mismatch return values can be described as in the following table: 

Files Relationship	Files.mismatch(Path, Path)
Same File	-1 (match)
Copied File	-1 (match)
Different Files, same content	-1 (match)
Different Files, different content	>0 (mismatch)
Soft-linked	-1 (match)
Hard-linked	-1 (match)

Now, let’s take a look at two examples. In the first one, we’ll create two identical files and compare them, but in the second one we will create two different files with different content. 

    @Test
    public void givenIdenticalFiles_thenShouldNotFindMismatch() throws IOException {
        Path filePath1 = Files.createTempFile("file1", ".txt");
        Path filePath2 = Files.createTempFile("file2", ".txt");
        Files.writeString(filePath1, "Java 12 Article");
        Files.writeString(filePath2, "Java 12 Article");

        long mismatch = Files.mismatch(filePath1, filePath2); // match
        assertEquals(-1, mismatch);
    }
    
    @Test
    public void givenDifferentFiles_thenShouldFindMismatch() throws IOException {
        Path filePath1 = Files.createTempFile("file1", ".txt");
        Path filePath2 = Files.createTempFile("file2", ".txt");
        Files.writeString(filePath1, "Java 12");
        Files.writeString(filePath2, "Java 12 Post");

        long mismatch = Files.mismatch(filePath1, filePath2); // mismatch -> return with byte size of smaller file
        assertEquals(7, mismatch);
    }

2. Java 12: Collectors.teeing() in Stream API 

It is a new static method teeing to java.util.stream.Collectors interface which allows to collect using two independent collectors and then merge their results using the supplied BiFunction. 

Every element passed to the resulting collector is processed by both downstream collectors. Then their results are merged into the final result using the specified merge function. 

Please note that this function helps in performing a certain task in a single step. We can already perform the given task in two steps if we do not use the teeing() function. It’s just a helper function to reduce verbosity. 

@Test
    public void givenArrayOfIntegerValues_thenCalculateMinAndMaxValue() {
        List<Integer> nums = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9);
        Map<String, Optional<Integer>> collect =      nums.stream().collect(Collectors.teeing(
                Collectors.minBy(Comparator.comparing(i -> i)),
                Collectors.maxBy(Comparator.comparing(i -> i)),
                (min, max) -> Map.of("min", min, "max", max)));
        assertEquals(1, collect.get("min").orElse(-1));
        assertEquals(9, collect.get("max").orElse(-1));
    }

3. Java 12: Compact Number Formatting 

CompactNumberFormat is a concrete subclass of NumberFormat that formats a decimal number in its compact form based on the patterns provided by a given locale. 

public static NumberFormat getCompactNumberInstance(Locale locale, NumberFormat.Style formatStyle)

The locale parameter is responsible for providing proper format patterns. The format style can be either SHORT or LONG. For a better understanding of the format styles, let’s consider number 1000 in the US locale. The SHORT style would format it as “10K”, and the LONG one would do it as “10 thousand”. 

Now let’s take a look at an example that’ll take the number of likes under this article and compact it in two different styles: 

    @Test
    public void givenNumber_thenFormatAsCompactValues() {
        NumberFormat likesShort = NumberFormat.getCompactNumberInstance(Locale.US,
NumberFormat.Style.SHORT);
        likesShort.setMaximumFractionDigits(2);
        assertEquals("5.5K", likesShort.format(5500));

        NumberFormat likesLong =
        NumberFormat.getCompactNumberInstance(Locale.US, NumberFormat.Style.LONG);
        likesLong.setMaximumFractionDigits(2);
        assertEquals("5.5 thousand", likesLong.format(5500));
    }

We can also parse compact numbers into long patterns: 

    @Test
    public void givenCompactValues_thenFormatAsNumber() throws ParseException   {
        NumberFormat fmt = NumberFormat.getCompactNumberInstance(Locale.US, NumberFormat.Style.LONG);

        assertEquals(5500L, fmt.parse("5.5 thousand"));
}

4. Java 12: Java Strings new Methods – indent() and transform() 

intent() method adjusts the indentation of each line based on the integer parameter. If the parameter is greater than zero, new spaces will be inserted at the beginning of each line. On the other hand, if the parameter is less than zero, it removes spaces from the beginning of each line. If a given line does not contain sufficient white space, then all leading white space characters are removed. 

    @Test
    public void givenString_IntentStringWithNumberThatGreaterThanZero() {
        String text = """
                Hi Symphony
                 Solutions
                      """;//First line does not have any space but in second one has 1 space in the beginning of line
        String newText = text.indent(1);//it adds  space in each line,therefore length should be increased plus 2
        assertEquals(text.length() + 2, newText.length());
    }

    @Test
    public void givenString_IntentStringWithNumberThatLessThanZero() {
        String text = """
                Hi Symphony
                 Solutions
                       """;//First line does not have any space but in second one has 1 space from beginning of line
        String newText = text.indent(-2); //it removes space in each line,therefore length should be decreased by 1
        assertEquals(text.length() - 1, newText.length());
    }

Transform() method allows us to call a function on the given string. The function should expect a single String argument and produce an R result. 
Let’s look at an example where we will use transform() method to convert a CSV string to the list of strings: 

    @Test
    public void givenString_thenConvertListOfStrings() {
        String text = "Hi,Symphony,Solutions";
        List<String> strList = text.transform(s -> Arrays.asList(s.split(",")));
        assertEquals(strList.size(), 3);
    }

Java 13 features 

5. Java 13: FileSystems.newFileSystem() Method 

public static FileSystem newFileSystem(Path path, Map<String, ?> env) throws IOException 

Java FileSystems newFileSystem(URI uri, Map<String, ?> env) constructs a new file system that is identified by a URI. This method iterates over the FileSystemProvider installedProviders() providers to locate the provider that is identified by the URI URI getScheme() of the given URI. 

    @Test
    public void givenFileName_thenCreateFileSystem() throws IOException {
        String zipFilename = "test.zip";
        final Path path = Paths.get(zipFilename);
        final URI uri = URI.create("jar:file:" + path.toUri().getPath());

        final Map<String, String> env = new HashMap<>();
        env.put("create", "true");
        try (FileSystem fileSystem = FileSystems.newFileSystem(uri, env)) {
            assertTrue(fileSystem.isOpen());
        }
    }

6. Java 13: DOM and SAX Factories with Namespace Support 

New techniques for creating DOM and SAX factories that support Namespaces have been added. 

//java 13 onwards  
DocumentBuilder db = DocumentBuilderFactory.newDefaultNSInstance().newDocumentBuilder();   
// before java 13  
DocumentBuilderFactory dbf = DocumentBuilderFactory.newDefaultInstance();   
dbf.setNamespaceAware(true);   
DocumentBuilder db = dbf.newDocumentBuilder();  

Java 14 features 

7. Java 14: Switch expressions 

Java 14 takes the switch to the next level. No longer a simple statement, it is now an expression, and so it can return a value! And by making this improvement we no longer have to think about break; Should one of your cases feed into a block of code, yield is used as the return statement of the Switch Expression, and we can assign this value to the corresponding variable. 

    @Test
    public void givenString_thenTestSwitchWithExistingCaseValue() {
        String color = "Blue";
        String adjacentColor = switch (color) {
            case "Blue", "Green" -> "yellow";
            case "Red", "Purple" -> "blue";
            case "Yellow", "Orange" -> "red";
            default -> {
                System.out.println("The color could not be found.");
                yield "Unknown Color";
            }
        };
        assertEquals(adjacentColor, "yellow");
    }

8. Java 14: Helpful NullPointerExceptions 

Previously, the stack trace for a NullPointerException didn’t have much of a story to tell, except that some value was null at a given line in a given file. 
Though useful, this information only suggested a line to debug instead of painting the whole picture for a developer to understand just by looking at the log. 
Now Java has made this easier by adding the capability to point out the name of the call that threw the exception, as well as the name of the null variable. 

int[] arr = null; 
arr[0] = 1; 

Earlier, on running this code, the log would say: 

Exception in thread "main" java.lang.NullPointerException 
at com.baeldung.MyClass.main(MyClass.java:27)

But now, given the same scenario, the log might say: 

java.lang.NullPointerException: Cannot store to int array
because "a" is null
As we can see, now we know precisely which variable caused the
exception.

Java 15 features 

9. Java 15: Text Blocks 

Starting from 2020, Java 15 finally supports text blocks! I am so excited by the fact that I no longer have to type dozens of “+” signs. I can simply do this instead: 

String json = """ 
"name":"Mammadali", 
"surname":"Alizada" 
"""; 

Text blocks now have two new escape sequences: 

1. \: to indicate the end of the line, so that a new line character is not introduced 
2. \s: to indicate a single space 

String multiline = """ 
A quick brown fox jumps over a lazy dog; \ the lazy dog howls loudly."""; 

String json = """ 
"name":"Mammadali", 
"surname":"Alizada" 
"""; 

In this example, we have improved readability of the sentence for the human eye, and no new line is added ->after “Alizada”.
Finally, text blocks only blocks, you can’t use them in a single line. 

10. Java 15: Hidden Classes 

A new feature being introduced in Java 15 is known as hidden classes. While most developers won’t find a direct benefit from them, anyone who works with dynamic bytecode or JVM languages will likely find them useful. 
The goal of hidden classes is to allow the runtime creation of classes that are not discoverable. This means they cannot be linked by other classes, nor can they be discovered via reflection. Classes such as these typically have a short lifecycle, and thus, hidden classes are designed to be efficient with both loading and unloading. 
Note that current versions of Java do allow for the creation of anonymous classes similar to hidden classes. However, they rely on the Unsafe API. Hidden classes have no such dependency. 

Java 16 features 

11. Java 16: Pattern matching for instanceof 

Pattern Matching is a means to get rid of needless casting after an instanceof condition is met. JVM automatically casts the variable for us and assigns the result to the new binding variable. For example, we’re all familiar with this situation: 

if (o instanceof Car) {
System.out.println(((Car) o).getModel());
}

Which, of course, is necessary if you want to access the Car methods of o. That being said, it is also true that in the second line, there is no question that o is a Car – the instanceof has already confirmed that. So, with Pattern Matching, a small change is made: 

if (o instanceof Car c) { 
System.out.println(c.getModel()); 
} 

You can even use Pattern Matching in the same line as the instanceof: 

public boolean isHonda(Object o) {
	return o instanceof Car c && c.getModel().equals("Honda");
}

12. Java 16: Add Stream.toList Method 

The aim is to reduce the boilerplate with some commonly used Stream collectors, such as Collectors.toList and Collectors.toSet. 

List<String> integersAsString = Arrays.asList("1", "2", "3");
List<Integer> ints = integersAsString.stream().map(Integer::parseInt).collect(Collectors.toList()); //previous version
List<Integer> intsEquivalent = integersAsString.stream().map(Integer::parseInt).toList(); //new version

Java 17 features 

13. Java 17: Sealed classes

Sealed classes are somewhat similar to final classes. When sealed keyword is accompanied by permits keyword, this indicates that a class or an interface can be extended or implemented only by specified classes or interfaces. Using sealed classes allows developers to know all possible subtypes supported in the given format. 

Let’s take a look at a simple example: 

public sealed interface Expression permits Addition {
    double perform(double x, double y);
}

final class Addition implements Expression, Serializable {
    @Override
    public double perform(double x, double y) {
        return x + y;
    }
}

//This statement gives compile error, because Expression interface does not allow to Log subtype
final class Log implements Expression {  
    @Override
    public double perform(double x, double y) {
        return 0;
    }
}

14. Java 17: Records 

Records are data-only classes that handle all the boilerplate code associated with POJOs. We can avoid all the unnecessary boilerplate code by using Records. Even though it doesn’t seem like a huge enhancement, it drastically increases developer productivity. 

That is to say, a POJO class like the following: 

public final class Person {
    private final String name;
    private final int age;
    private final String profession;

    public Person(String name, int age, String profession) {
        this.name = name;
        this.age = age;
        this.profession = profession;
    }

    public String name() {
        return name;
    }

    public int age() {
        return age;
    }

    public String profession() {
        return profession;
    }

    @Override
    public boolean equals(Object obj) {
        if (obj == this) return true;
        if (obj == null || obj.getClass() != this.getClass()) return false;
        var that = (Person) obj;
        return Objects.equals(this.name, that.name) &&
                this.age == that.age &&
                Objects.equals(this.profession, that.profession);
    }

    @Override
    public int hashCode() {
        return Objects.hash(name, age, profession);
    }

    @Override
    public String toString() {
        return "Person[" +
                "name=" + name + ", " +
                "age=" + age + ", " +
                "profession=" + profession + ']';
    }
}

Becomes this: 

public record Person(String name, int age, String profession)
{}

We no longer have to think about adding hashCode(), equals(), toString(). These are usually easily generated by a favorite IDE, but still for me this was a long-awaited feature! 

As you can see, Records are both final and immutable – there are no extending Records, and once a Record object is created, its fields cannot be changed. You are allowed to declare methods in a Record, both non-static and static, and implement interfaces just like with final classes. 

Conclusion 

Java remains the top programming language that is widely used by software programmers and is often recommended as the starting point for those just starting out in their careers. In this article, we have overviewed updates from Java 12 to Java 17. Of course, this doesn’t cover everything, but mostly the few that I found interesting. Hopefully, this gives you the gist of what they’re capable of doing. Looking at the improvements Java has delivered in one go, we can be sure that the Java platform is well-positioned for further development and growth in the cloud. So, if you want to become a software developer, you must learn Java to stay on track with the new developments. 

Reactive applications are scalable since workload grows and also resilient when failures arise. If an application has both scalability and resiliency, it means that the application is responsive. 

Vert.x is a toolkit used for building reactive applications on the JVM using an asynchronous and non-blocking execution model. As it is based on Netty, which is an event-driven and asynchronous network application framework, Vert.x helps build reactive applications using the event loop concept, which is an implementation of a Multi-Reactor pattern. 

An event bus always listens for new events. Whenever a new event arrives, it dispatches it to some handler that knows how to handle it.

The image above indicates a traditional implementation of the Reactor pattern executed by a single thread and reducing the concurrency issues as the pattern that guarantees no handlers is executed concurrently. However, it also implies that the event loop must never be blocked by any of the execution handlers since otherwise, it can decrease the whole system performance significantly. Vert.x brings a good approach here by using a Multi-Reactor pattern, which means using multiple event loops running on separate threads while still guaranteeing that no execution handlers will be executed concurrently. Multi-Reactor patterns use multiple event loops, which requires more CPU cores to increase performance.  

Another case is that even when we use multiple event loops, some events can still take a long time to be processed. In this case Vert.x provides using worker threads for costly executions.  

Vert.x Terminology  

Vert.x has its defined terms. Let’s go over the most well-known terms below:  

• Verticle 

Vertical is a part of Vert.x architecture. It’s a piece of code that can be deployed to Vert.x and is executed in a single thread. It can be considered as a controller handling servlet requests, and all communications are executed with events asynchronously.  

Vertical types:  

Standard verticles: All the code is executed within the same event loop.

Worker verticles: Not executed within an event loop, executed using a thread from the Vert.x worker thread pool. We should not block the event loop for long-running tasks, so it can use a thread from the Vert.x worker thread pool and not block the event loop at all. They can be executed by different threads at different times.

How to deploy a worker verticle?

You need to create an object from DeploymentOptions class and set its worker property as true. Finally, the deployment options must be used while deploying the verticle. Please check the simple example below: 

• Vert.x Instance  

A verticle is executed within a single Vert.x instance, which is executed on the JVM instance. If there are a lot of verticles, they will be simultaneously executed in the same Vert.x instance. Moreover, it is feasible to make multiple Vert.x instances clustered using Event Bus. 

It is like a container for verticles and also an entry point to API. 

• Concurrency  

The Vertical instance always promises that it is executed within the same thread. No deadlock or race condition will occur.  

• Event Loops  

Vert.x instance manages the number of thread pools to the number of CPU cores very closely.  

• Event Bus  

It is a lightweight distributed messaging system used for point-to-point communication, publish/subscribe messaging model, request/response model.

• Shared Data  

Passing and sharing data between threads is a very important concept in asynchronous programming. Vert.x provides both synchronous and asynchronous shared data functionality for this. The shared data feature enables users to use shared data between different verticles for the same Vert.x instance or a cluster of Vert.x instances.  

Vert.x uses Shared Map for global access of data.  

Vert.x Features  

Let’s look over a list of features characteristic of Vert.x that make it the framework of choice for many developers. What are the advantages of using Vert.x?  

• Handling concurrency and high scalability  

Vert.x is a lightweight event-driven and non-blocking application framework that allows developers to handle a lot of concurrency using a small number of kernel threads, so it increases the scalability of the application significantly.  

• Polyglot 

Vert.x is similar to Node.js but it is both polyglot and reactive on the JVM. It supports multiple languages like Java, JavaScript, Apache Groovy, Ruby, Scala, Kotlin. 

• Automatic failover(Resilient)  

In a cluster environment, if running a Vert.x instance fails, it will be automatically restarted on another node of the cluster. 

• Fast  

The way Vert.x addresses that problem is that any I/O work is put in a queue. Since putting a new task in a queue is not a particularly heavy operation, Vert.x is able to process hundreds of thousands of those per second.  

• Ecosystem  

Web APIs, databases, messaging, event streams, cloud, registries, security…  you name it. Vert.x has you covered with a comprehensive end-to-end reactive clients stack for modern applications.  

• Clustering  

A cluster manager is responsible for the discovery of nodes and sharing data within different Vert.x instances.  

To use a cluster manager programmatically in Vert.x: 

• Documentation  

The official Vert.x documentation is very extensive, nicely grouped by topic and with many manuals and examples in multiple programming languages. These examples are usually kept quite simple, which makes sense to not overwhelm the reader and to focus on one certain tool, as well as look over Vert.x use cases one at a time. 

• Lightweight  

It is very easy to integrate Vert.x into your project and its core library. It can also be used in a Spring Boot project, so you can take advantage of a dependency injection feature of the Spring framework inside a Vert.x project. 

Benchmarks for Vert.x and Spring Boot Framework 

The following are some examples of Vert.x vs Spring Boot uses, that illustrate how the two frameworks perform under various conditions with different concurrency levels. 

Vert.x example: 

Spring Boot example: 

Vert.x Test1	Spring Boot Test1
Concurrency Level: 10  Time taken for tests: 58.640 seconds  Complete requests: 1000000  Requests per second: 17053.09 [#/sec]  Time per request: 0.586 [ms]	Concurrency Level: 10  Time taken for tests: 205.175 seconds  Complete requests: 1000000  Requests per second: 4873.89 [#/sec]  Time per request: 2.052 [ms]

As you can see in the first test, Vert.x is significantly faster compared to Spring Boot. Let’s have a look at the CPU and memory usage during the test: 

• CPU and memory usage for Vert.x: 

• CPU and memory usage for Spring Boot: 

As expected from a lightweight framework, Vert.x consumes much less CPU and memory over time. But this is just a very simple test case. More interesting is the second test: 

Vert.x Test2	Spring Boot Test2
Concurrency Level: 10  Time taken for tests: 103.471 seconds  Complete requests: 10000  Requests per second: 96.65 [#/sec]  Time per request: 103.471 [ms]	Concurrency Level: 10  Time taken for tests: 106.633 seconds  Complete requests: 10000  Requests per second: 93.78 [#/sec]  Time per request: 106.633 [ms]
Concurrency Level: 100  Time taken for tests: 10.488 seconds  Complete requests: 10000  Requests per second: 953.46 [#/sec]  Time per request: 104.881 [ms]	Concurrency Level: 100  Time taken for tests: 11.779 seconds  Complete requests: 10000  Requests per second: 848.99 [#/sec]  Time per request: 117.787 [ms]

Because of the 100 ms waiting time in it, the minimum time per request is 100 ms in this test, so only the additional time is relevant here. With 10 concurrent requests, you can already see a slight advantage for Vert.x, but with 100 concurrent requests, the difference is much more obvious. Again as expected, Vert.x is significantly faster. Due to the use of fewer threads, Vert.x with its non-blocking approach performs a lot faster than Spring Boot, which needs to create at least 100 parallel threads in this example. 

What is the Future of the Vert.x Project?  

Vert.x has very good documentation that makes it easy to understand every detail with the help of simple examples. At the moment, it makes it a good option to use in terms of easiness of deployment and super performance as an async server. There is a great community with contributors who continue to fix the issues and release new features in Vert.x. It is also supported by the Eclipse Foundation community team. The strong community that has emerged around Vert.x makes it very easy to discover all its advantages, whether it’s through studying documentation or any Vert.x tutorial or example you may need to further explore the possibilities. 

Conclusion  

To summarize, Vert.x is a great toolkit when you want a non-blocking strategy in an event-driven architecture. It works with verticles, which provides a thread-safe environment. Vert.x makes it easy to write concurrent applications that scale effortlessly from a single low-end machine to a cluster with several high-end servers. Add the fact that you can use the most popular languages for the JVM, and you have a web developer’s dream come true! 

The online casino and gambling industry has changed significantly in recent years, with increased levels of regulation and a continued increase in focus on the topic of responsible gambling. The industry is keen to distance itself from the negative image associated with it in the past and to focus on ensuring that a safe, responsible, and entertaining experience is provided to users.  This change is constant and operators and platform providers need to keep abreast of the continual changes and updates in the regulatory frameworks across multiple jurisdictions. 

Where does Symphony Solutions sit within the iGaming value chain?  

The iGaming division of Symphony Solutions works with operators who are licensed and regulated and who do not carry out illegal operations. These operators are our customers. The customers of the operators are the people that register with the operator and who bet on sports or entertain themselves, playing the online casino games available on the operator websites.

FAQ 1:  Gambling is not legal in my country; can I work for Symphony Solutions? 

Absolutely, Symphony Solutions is not a Gambling operator, we are an international IT services provider and do not require a Gambling Licence. 

Responsible Gambling 

In addition to increased levels or regulation in a number of countries along with numerous states within the United State of America, the other area of ongoing maturity is that of Responsible Gambling. 

Over recent years a set of socially responsible initiatives were created to ensure the fairness and integrity of gambling operations were in place and to promote the awareness of problem gambling to end-users. These Responsible Gambling initiatives are active across governments, through control boards, and with the operators of casinos and sportsbetting operations. 

Broadly speaking the key areas that sit beneath the umbrella of Responsible Gambling are: 

• The exclusion of underage users – ensuring registration of underage users is not possible. 
• The protection of players at risk – making it easy for players to self-exclude themselves or to put in place financial limits on spending or time. 
• Accountable Marketing – making sure for example that advertising and marketing activities do not target children or players at risk that have self-excluded themselves. 
• Data privacy – ensuring that the end users’ data is private and that it remains private. 
• Online payment protection – ensuring that the system is reliable and secure so that customers can deposit, withdraw and transfer funds safely. 

Responsible Gaming procedures vary from country to country, in Sweden operators can set betting limits based on the financial information checks on end-users. In the UK the use of credit cards to pay for gambling is restricted. 

Our customers share a common goal, that goal is to ensure that gambling needs to be done at a fun and safe level, and we stand behind that ethos. 

FAQ 2: What about technology? 

The technology stacks being used are modern, often at the cutting edge. They are driven by new product development and are heavily influenced by the enormous size and scale of the systems required to support many thousands of transactions per second. It is not unusual to witness over twenty thousand transactions per second at peak times. 

Many of our teams are working to design and develop brand new products, products that are not only first to market but patented in multiple countries. Working on these new products will do wonders for your CV. 

From an infrastructure perspective, the typical iGaming systems are large and complex with a mixture of on-premise and cloud-based components.  AWS and Microsoft Azure are commonly used by iGaming operators that wish to leverage the benefits of cloud solutions. 

The advantages of working with iGaming projects from a development perspective: 

• Dynamic, highly transactional, large enterprise systems designed to work at scale.
• Great opportunity to grow both from technical and organizational perspective (working in an Agile environment). 
• Highly performant systems, with significant peak loads during large sports events. 
• Environment changes very fast (from MVP to release). 
• Working with top technologies – Serverless, Cloud, BI-Datawarehouse, etc.
• Being part of the design, delivery and launch of unique, first-to-market products. 

FAQ 3: Are Operators Agile? 

In short, the answer is yes. Whilst some are further along in their agile journey than others, most have recognized the benefits of agile product development and are familiar with working in a lean-agile manner. The majority of our customers have a number of SCRUM teams with some using Kanban. Other larger operators have worked with the Scaled Agile Framework utilizing multiple release trains. 

FAQ 4: What direction is the industry going? 

From a product development perspective, our customers want to continue to delight users, with new innovative games, games that feature live video streaming, so that customers can engage and immerse themselves in real-time. We are seeing a shift towards hybrid applications that bring elements of traditional games together with more modern game features, all designed to bring further excitement, and engagement with the end-users. 

From a geographical perspective, the growth in the US market is impressive, with many states having already legalized gambling we are seeing further growth as new states come on board. For these US-based operators, we see a focus on the user experience, with an understanding that a “using a copy and paste” approach of a European user doesn´t translate well to a US player experience. As a result, strong UX/UI and front end development skills are in demand. 

FAQ 5: OK I’m interested to hear more! What projects are available right now? 

If you join Symphony Solutions you´ll be working for us and representing Symphony Solutions, helping to design and develop new products and applications for operators both in a business to consumer (B2C) environment and in business to business (B2B) focused organizations. 

We have a number of exciting projects working with top iGaming operators. 

From those based in the USA developing B2B solutions for the Casino and Sportsbetting verticals to those based in Malta with B2C brands. 

We have outlined a couple of them here for you to give you a taste of what is available. 

Avantage Entertainment – a top-tier US-based operator in both the B2C and B2B space.  
Casumo – a highly respected Malta-based operator with both online casino and sportsbetting products.  

Be sure to bookmark our vacancies for updates and information on the many other roles that we have. If you see a role that either you or indeed one of your friends might be suitable for, then share or apply! The future is bright.

With the rise of personal technology devices, a mobile phone has grown to be from a mere convenient means of communication to somewhat of a personal assistant. The smartphones of today for many professionals can even replace desktop devices or other “heavy duty” technologies.  

According to Statista, the number of mobile devices users in the last decade grew from 1,06 to 3,8 billion and is expected to further grow by hundreds of millions per year. 

Always on the go, you barely have to reach to your pocket and find a plethora of apps to assist you with school and work, lifestyle and entertainment. But, of course, for the smartphone owners to make good use of these apps, the development team’s job is to make sure that the mobile app is functioning without a hitch. A bug-free app speaks for itself and earns the company a good reputation and the trust and loyalty of their customers. This is where mobile app testing comes into play. 

Mobile application testing is a process of detecting defects, bugs in software that has been developed for the use on handheld mobile devices (most commonly, applies to smartphones and tablets). It is tested for functionality, usability and consistency. Mobile apps can come pre-installed on the device or may need to be downloaded and installed from software distribution platforms (e.g., Play Market, App Store). 

The team of test engineers applies QA best practices to ensures the quality of the app on every step of the development process. Let’s look over some ways of how to test mobile applications that are most common to use when preparing them to go out on the market. 

Types of Mobile QA Testing 

There are two types of mobile testing that need to be done:  

1. Hardware testing. Internal hardware of the device is tested, including processors, memory space, battery, screen size and resolution, camera, sensors, card slots, etc. 
2. Software or Application testing. The main purpose it to test the functionality of the mobile applications. Here you will need to differentiate between the types of mobile apps such as: 
   • Native apps – created to be used only on one platform (e.g., mobile, tablet)  
   • Mobile web app – server-side apps that allow to access the web on mobile via browsers (Safari, Chrome, etc.)
   • Mobile web app – server-side apps that allow to access the web on mobile via browsers (Safari, Chrome, etc.) 

These types of apps differ in the way they are developed and how they can be used and tested: 

• Native apps are created with a specific platform in mind whereas mobile web apps should work cross-platform.
• Native apps are developed with native SDKs, while for mobile web apps developers can use basic web technologies.
• Native apps are updated from Google play store or app store, while with mobile web apps this is done centralized.
• Native apps need to be installed on your mobile device; mobile web apps can be accessed and used without installation via mobile web browser.
• Mobile web apps can only be used with an internet connection, while native apps typically don’t require this. 

Why Mobile Testing is Important 

When testing applications on mobile devices, the QA engineer can face some challenges that are linked to the following nuances: 

• Wide range of mobile devices – various screen sizes and resolutions, hardware configurations, different brands especially for Android devices (HTC, Samsung, Huawei, Xiaomi, etc.); 
• Different mobile operation systems – Android, iOS, Windows, BlackBerry; 
• Different OS versions – IOS 10.x, IOS 11.x, Android 9.x, Android 10.x, etc. 

Types of Mobile Application Testing 

Mobile applications testing usually is designed to test for many different characteristics. Let’s look at some of the common types of testing to be used: 

1. Compatibility testing is handled on different OS versions, devices, screen sizes, cross-browser testing, etc. 
2. UI testing checks the theme and style of the interface correctness, buttons and menus, responsiveness of the app, etc. 
3. Performance testing analyses app performance with different network connection, battery usage, file sharing, etc. 
4. Installation tests checkhow the app behaves when repeatedly installed and deleted on the device.
5. Operational testing is designed to prove the stability and reliability of the app (e.g., data loss when updating from the store, backups and restoration for when the battery goes down, etc.).
6. Security testing performs to see if the user data is protected.
7. Usability testing detects if the application is simple in use, navigation flows are easy to understand, user experience is satisfactory. 

What else you should focus on while testing mobile apps: 

• How the application will work when switching to online/offline mode. 
• When something interrupts the application: 
  • Notifications from other apps.
  • Incoming phone calls.
  • Forced system updates.
• When you switch from album to portrait view or the other way around.
• Device going into sleep mode.
• When you lock/unlock your devices while the application is running in the background.

Basic Differences in App Testing: Android vs iOS 

1. First of all, there are a lot of different Android devices on the market with a huge variety of sizes, screen resolutions, etc. Basically, every company has its own standards.  
2. It’s much harder to develop and test on Android devices because you should make sure that every image, button, and other elements of the interface are not misaligned. 
3. Because of different companies developing on Android, software updated is not synchronized which can lead to problems with supporting. 
4. And of course, it leads us to the problems with security because of the variety of devices with Android.

What are the Differences Between Testing on a Real device vs. Simulator vs. Emulator 

Both simulators and emulators are virtual devices. A virtual device is a software which allows providing the same functions as a real device, although there can be differences with some functionalities like a phone camera, etc. 

Advantages of a Simulator/Emulator  

• When the deadline is coming and you have no time for purchasing a real device. 
• The vast numbers of mobile devices from different manufacturers creates problems, whereby the testers are not confident about which mobile devices to invest in for testing, considering the budget constraints. Emulators and simulators are tailor-made to specifically address this situation. 
• In most cases they are free.
• It’s easier to do web application testing, just copy and paste the link. 

Disadvantages of a Simulator/Emulator 

• You can’t verify battery scenarios.
• You are not able to simulate any interceptions like phone calls, SMS, etc. 
• They are not able to simulate the exact color display.
• Simulating memory-related issues.

Advantages of the Real Device 

You could say that all of the disadvantages of a Simulator/Emulator are an advantage of a real device. You can test all real based scenarios and be sure that the issue is not related to Simulator/Emulator specifics. 

Disadvantages of the Real Device 

• Money. Physical devices need to be purchased and to make sure that you are covering most of the popular devices, it will cost you much more than using some virtual devices. 
• There is a great variety of mobile devices from Apple and Samsung to Pixels and Symbian, and so on. With all this wide range of mobile devices and operating systems, it is very hard for the software testing team to arrange testing for all sorts of mobile devices while working under a considerable amount of budget and timeline related constraints. 

Mobile Application Testing Tools to Get you Started 

There’s an abundance of great tools that you can work with but here are some of the essentials that you might want to consider trying: 

• Charles proxy – Charles is an HTTP proxy / HTTP monitor / Reverse Proxy that enables the tester to view all of the HTTP and SSL / HTTPS traffic between their machine and the Internet. It’s a great tool to look for logs while you are using your application; 
• Android studio – it has the same functionality as Charles proxy. All you need is to connect your Android device with a wire and all the logs will be displayed in the Android studio. Works only with Android devices; 
• Xcode – mostly used for development purposes, but you can use it as an emulator of iOS devices. 

Bottom Line 

Mobile application testing comes as an important part of the product development process. How you approach it will determine whether your app will be a barely functional novelty or a daily staple in your customer’s life. For instance, Ladbrokes Coral focused on performance testing when preparing for UK’s greatest annual racing event and managed to support over two million user visits with no downtime, securing the brand’s reputation and popularity with the customers and increasing revenue. So, if you want to achieve similar results and see your app succeed, remember the following steps: 

• Identify what platforms you will support. 
• You should have at least a couple of physical devices. 
• Use tools which will help you with testing.
• Use different simulators/emulators to expand your testing coverage. 

Don’t forget that you are creating something that will potentially bring a better quality of life for your users. Your part is to ensure the good quality of the mobile app. If you want to help create high-quality mobile applications to satisfy even the most peculiar user, see our open QA vacancies and apply. 

Client interview has recently become an integral part of the hiring process, especially in the IT field. The most frequently asked questions from the interviewees sound like “Isn’t it the same as HR or Behavioral Interviews?”. The answer is yes and no. The main difference is in shifting the focus from general answers, even very well-shaped, to the customer’s needs and requirements as a primary source of prioritizing your skills.

Here are the most common areas of expertise that are covered during the client interview process, and some of them are not so obvious as they might seem at first sight.

Previous Experience

One of the popular client interview questions that you will hear, is some variation of, “Tell me about your job experience.” This question is more loaded than you might think — the interviewer may want to know how this experience has prepared you for the potential job. Thus, the main area of interest for any client lies in getting a better understanding of how your background relates to the position they are looking to fill.

It’s crucial to note that preparation for this question starts with compiling the resume. The primary idea is to point out and focus on that expertise which is of vital importance for both the client and the project instead of including all the skills and abilities that might be useful in general, but minor in terms of the project requirements.

This might seem not so easy; however, with a little preparation, you will be able to succeed.

Prepare your “elevator pitch” beforehand. Use short and simple sentences and active statements to demonstrate confidence and proactivity. Concentrate on the expertise the client is looking for. Quantify your experience. If applicable, use data to add proven value to your accomplishments. Illustrate your potential by showing how your previous work is directly connected to the requirements of the position in question. End with a goal statement. Mention what you want to achieve in this position. Be honest, BUT confident.

Past Failures

This is a typical behavioral job interview question, and such questions might be difficult. Negative experience is tough to talk about because your focus is on trying to present yourself from the best side. However, here is the main trick – by asking such a question, a client wants to decipher how good you are at failing smart and recovering, but not what your failure is in fact.

Talk about how you’ve used the experience to become better at your job and to avoid similar mistakes, provide examples. You may want to use the STAR method (Situation/Task, Approach and Results). Be short, about 2-3 min, and avoid talking about anything that jeopardizes your or your employer’s credibility. Pick a real failure but not anything too costly. Stay honest. Choose an issue relevant to the client’s area of interest.

Current Job

Despite a very clear message, this question is aimed at unveiling several aspects which the client is most interested in. Primarily, the idea of these questions is to investigate what level of problems the person works on – if it is operational/detailed, team level or strategic level.

Another area of interest is a candidate’s motivation for changing jobs – moving towards new opportunities or escaping from the job they dislike. It is a very sensitive issue that should be faced with a positive approach. Use the proper language and shift your mind and message from “I am dissatisfied with my current team collaboration” to “I am looking for a highly collaborative team”. Such a maneuver helps to “sell” yourself as a person who is looking for solutions instead of someone to blame.

Favorite Project

The client investigates how the position that they have fits with what you love and can do best, with what excites you.

You should want to balance what a client wants to hear (so you must read the position requirements in advance) with showing your real self, what drives you and what makes your eyes sparkle. This has to be at least to some extent an emotional conversation. Keep in mind that any hiring process is a “dialogue”, not a “monologue”. You need to show that you are a good fit for the position, but at the same time make sure that you feel it’s your cup of tea, otherwise, there is a threat of potential burnout or dissatisfaction.

Are You a Good Match? 

Sounds like a question in the competition, doesn’t it? Actually yes, but be careful! The majority of people will start talking about themselves: ‘because I am…” NO! Start with: “Because you said you need someone with expertise in architecture, right? Well, I did …” Give examples. START with a “you” statement.

When answering this type of questions, always talk about clients’ needs first, and only after tell about your unique expertise.

Don’t compare yourself to others. The only comparison option you might want to use is to show how the company will benefit by applying your expertise.

Motivation 

What, in your experience, motivates you best? What is your most successful job performance? Can you give us an example of this motivation in action in the workplace? You should want to show your strong sides as well as tell about the activities which keep you engaged.

Also, the client will want to see how you know to deal with disengaging environments, either tasks or people. It is easy to be productive while you are engaged, but how do you tackle issues if the conditions are reversed? Here your strategy is to show that you are adult and mature, that you know how to get yourself doing the things that you have committed to fulfil.

Expectations/Questions to the Client

Traditionally, any interview finishes with the questions to the client. Surprisingly, the majority of interviewees either neglect or underestimate those questions. This part of the interview seems kind of formal, however, it might be a perfect chance to bridge the company vision with the interviewee’s expectations.

Prepare questions to ask the client in advance. Look up their website to see what values they prescribe to the company and the client. Talk of a bigger purpose, company values that you want to be a part of. Talk about opportunities to contribute to others. Talk about opportunities to learn. In this sequence: being part of a big goal, contributing, learning.

If the questions were already covered, express gratitude and give summarizing feedback. This will show your genuine interest in the position and unveil your active listening skills.

Client interviews are an excellent way to “match” the person to the project. Other steps in the recruiting process can only tell you so much about the candidate. If you are looking for someone to fit a certain role, it’s good to know that they are aligned with the client’s view on the position, scope of responsibilities, even the project itself. The potential candidate may have all the right skills and experience but not for this particular project or client. It’s important to be able to elaborate on all that during the interviewing process, so client interviews have now become a big step in the right direction. 

– Roman Shevchenko, Recruitment Lead

Preparation and Building Rapport 

Prior preparation is a key factor for passing the interview successfully. Here are several more general tips on how to succeed in the process of the interview.

Meet with the Client Partners, Recruiters, Service Delivery Managers or other people in the company who have been in contact with the client. Ask about previous interviews, customer specifics and their main focus of interest. Also, check official client accounts on social media, the website and other available resources to look for the inbound information which might help in building a connection. Don’t ignore even minor details, they might matter!

Make sure you are aware of the format of the conversation and prepare all the necessary equipment. Is it a phone call or a video conference? Are there any additional tools you may need to use?

Wear an appropriate outfit: no tracksuits or pajamas with panda bears!

Adjust to the client’s style of communication: formal, semi-formal, a lot/not much of a small talk. Try to mirror non-verbal cues and body language, it will help to connect.

Ask clarifying questions if you are not sure about the message. Ask to paraphrase, to elaborate on the details, to type the message in the chat. Use any possible means until you are 100% aligned.

Client interview is always both a challenge and a great opportunity to polish your presentation skills! Stay prepared and good luck!

Nobody likes it when apps glitch and freeze. Especially when it happens at the most inconvenient moment. When we book a taxi, bet on a game at the last second, or turn into the wrong lane while using maps navigation on our phones. All this makes people frustrated and creates a bad experience with the product, brand, apps, and as a result, the download statistics go down. 
Every developer knows, “When download statistics go down, it’s time to analyze why crash and ANR statistics are up”.
One of the reasons for glitches, crashes and freezes can be memory leaks. Now we will talk about how to identify them in Android applications and how to deal with them. 

What are memory leaks? 

Most Android native apps are written on Java or Kotlin. That means that we have a Virtual Machine with aGarbage Collector (GC). 

“Garbage Collection deals with finding and deleting the garbage from memory. However, in reality, Garbage Collection tracks each and every object available in the JVM heap space and removes unused ones.” 

A memory leak happens when memory is allocated but never freed. This means the GC is not able to take out the trash once we are done with the takeout.  
Android has a 16ms drawing window, and the GC normally takes less time to deal with memory. When the heap of allocated memory is increasing in unevenly large portions and memory is not being deallocated, the system will force a larger GC to kick off, which pauses the entire application’s main thread for around 50ms to 100ms. 

Does my application have memory leaks? 

First of all, we need to find and detect memory leaks in the application. Let’s look over some of the ways and tools you can use to do that. 

• Android Studio Memory Monitor 
  Android Studio provides handy tools for profiling the performance of your app. One such tool is the Memory Monitor. Open the bottom tab in Android Studio while the app is running on a device or emulator. You can see how much memory your app has allocated at the moment. Memory heap will be increasing and decreasing depending on the actions that the application does at the moment. If you notice that memory is allocated but doesn’t get deallocated in a short period of time, this is probably what we are looking for. 
• Infer 
  Infer is a static analyzer tool made by Facebook. This CI tool helps you find possible null pointer exceptions and resource leaks, as well as annotation reachability, missing lock guards, and concurrency race conditions. You can read more on the getting started page.  
• ANR statistics on Google Play Console 
  Some of ANR (“Application Not Responding”) may be caused by big heap allocating and UII freezes for more than 50 – 100ms. You can check them on Google Play Consoles or read more here. 
• Crashes with OutOfMemmoryException 
  This is a 100% proven indicator that you have a memory leak. You should check all the resources and Context-related variables used in classes that you see on the crash stack. 
• LeakCanary 
  With its vast knowledge of the Android Framework internals, LeakCanary has a unique ability to narrow down the cause of each leak. This helps developers dramatically reduce OutOfMemoryError crashes. You can read more on the getting started page.  

How to fix Memory Leaks? 

• Contexts 
  Android SDK is full of classes that have Context as a parent class. Such classes and abstractions can be called “context-related”. Official documentation defines Context as such: 

“Interface to global information about an application environment. This is an abstract class whose implementation is provided by the Android system. It allows access to application-specific resources and classes, as well as up-calls for application-level operations such as launching activities, broadcasting and receiving intents, etc.” 

What we need to remember about memory leaks + context is that everything relating to Context, such as Activities, Services, Application, has a certain scope and a limited lifespan. So we can’t hold a reference to these types of classes because the state is rapidly changing.  
GC can’t clean resources when the reference of Activity or Context is present, but Activity is Destroyed and does not exist for us. 

You should not:  

1. Avoid using static variables for views or context-related references. 
2. Pass a context-related reference to a Singleton class. 
3. Improperly use Context. Use applicationContext() instead of activity context or view context when it’s possible. For example, for Toasts, Snackbars. 
4. Use a weak reference of the context-related references when needed. 

• Static and View References 

Just forget about static when you are working with context-related classes. You don’t need it in Activity for Views, and you don’t need to hold Context everywhere.

If you still can’t manage your logic without such a principle, your architecture or pattern can be wrong for that scenario. Use official guidelines and look into Dependency Injection or app\data\domain modules. 

  /*
     * -> Static and View References 
     * Bad practice: Static context-related variables
     */
    companion object {
        private lateinit var textView: TextView
        private lateinit var activity: Activity
    }

• Unregistered listeners 

Listeners and receivers, such as BroadcastReceiver, LocationListener, Bluetooth and BLE listeners, have a strong reference to Fragment or Activity, and they hold them until you unregister them.  

Carefully check your onCreate (onCreateView), onStart, and onResume methods. These lifetime methods typically have registered events. For each registered event, you should have unregistered opposing methods onDestroy, onStop, onPause.  

/*
        * -> Unregistered listeners
        * Note: Take care about jobs and architecture to avoid leaks. Use Android Work manager
        * or use RxJava or any safe jobs scheduling library.
        */

        private lateinit var leakyCountDownTimer: CountDownTimer
        private lateinit var leakyRunnable : Runnable
        private lateinit var leakyThread : Thread

        override fun onCreate(@Nullable savedInstanceState: Bundle?) {
            super.onCreate(savedInstanceState)
            startTimer()
            startRunnable()
            startThread()
        }

        override fun onDestroy() {
            super.onDestroy()
            /*
            * Bad practice: Don't stop runnables, threads and other timers on onDestory/onStop/onPause
            */
        }

        fun startTimer() {}
        fun startRunnable() {}
        fun startThread() {}
    }
}

• Inner classes and anonymous classes

Here we need to notice that the outer class should never have a static reference to the inner class. The inner class should never have a hard reference to the outer class, especially when talking about context-related classes.

Anonymous classes, such as Handler().postDelayed() or classes that emit a new thread (Runnable, AsyncTask), should stop and close this thread before being destroyed. Take care of every thread and resource that you have created.

/*
     * -> Inner classes and anonymous classes
     * Note: Take care about navigation and architecture to avoid bad practices with context-related variables
     */

    class SomeActivity : AppCompatActivity() {

        override fun onCreate(@Nullable savedInstanceState: Bundle?) {
            super.onCreate(savedInstanceState)
            leakyClass = InnerClass(this)
            leakyClass.goSomeActivity()

             //Some jobs here..

            leakyClass.goSomeActivity()
        }


        /*
        * Bad practice: Context-related logic inside inner class that may produce creating/destroying
        */
        private inner class InnerClass(private val activity: Activity) {

            fun goSomeActivity() {
                activity.startActivity(Intent(activity, SomeActivity::class.java))
            }
        }

        /*
        * Bad practice: Static variables of inner class
        */

        companion object {
            private lateinit var leakyClass: InnerClass
        }
    }

• Singletons 

The memory leak could happen when we initialize the singleton from the activity and pass a long-lived context-related reference to the singleton constructor when we initialized it. Singleton class will hold a reference to Context after destroying the initial resource.

Try to hold applicationContext() instead, or use Context as a parameter of Singelton methods.

 /*
     * -> Singletons  
     * Bad practice: Not application context variable inside Singleton
     * Bad practice: Not destroying context inside onDestroy
     */
    inner class SingletonClass(var context: Context) {
        lateinit var instance: SingletonClass
    }

• Bitmaps

Inefficient use of Bitmaps without recycling it. Managing Bitmaps may easily throw OutOfMemoryException.

Find out more about memory management for Bitmaps in the official documentation here.

Conclusion

Most of the time we spend on user stories, development, and distribution. That’s why we focus on building features, functionalities, and the UI components of our apps. 
We forget to focus on the core issue like performance and quality of the app, which is a major part of an application. 
Try to indicate memory leaks in your application and listen to this simple advice on writing code without unnecessary dependencies and excessive use of resources. Thus, you will certainly be able to protect the application from memory leaks. 
So to rehash, if you want to avoid memory leak scenarios, you should incorporate in your work these best practices: 

• Take care of context. 
• Use common architectures for Android projects, such as Clean, ViewModel, or UseCase Architectures. A clean and structured project is the best way for avoiding most issues. Here is the official architecture guide from Google. 
• Delegate referencing to DI and inject context-related references. 

Find out more about Android Performance Patterns on the official Google Developer channel on YouTube. 

After being a software tester for more than two years, I’ve gained knowledge and indispensable experience that I wanted to consolidate by getting properly certified. So, at the end of last year, I set a goal to pass the ISTQB Foundation Level certification and successfully obtained my credentials in December 2020. 

What is ISTQB foundation level certification 

The International Software Testing Qualifications Board offers different levels of software quality assurance certifications. ISTQB Certified Tester is one of the offered standardized qualifications available for Foundation, Advanced, and Expert levels of accreditation. 

ISTQB Foundation Level is the first accreditation level that you can obtain by passing an exam. It covers the basis that is required to be certified as a software tester, including both the understanding of theoretical fundamentals and practical knowledge. 

The exam is a multiple-choice test that consists of chapters, classified by difficulty (K1, K2, K3). There is a total of 40 questions. The passing grade is 65%, meaning that you are required to answer correctly a minimum of 26 questions to get the qualification. The Foundation Level certification is valid for life so there is no need to retake the exam any time in the future. 

How long to study for ISTQB Foundation exam 

Speaking about time frames, intense preparation for the certification took me a little bit more than 1 month before the final test. I was spending about 2.5-3 hours a day to study, and that’s counting only weekdays. This is not taking into account the fact that I took a theoretical course at Udemy beforehand. 

Resources used for ISTQB exam preparation:

1. Syllabus

Syllabus is the basis of all that you need to know for the ISTQB Foundation Level exam. It consists of general objectives, glossary, learning objectives for different knowledge areas, key concepts, references to sources (literature, standards, etc.). The syllabus is available on the ISTQB official website.

However, the syllabus is not an exhaustive source of knowledge on software testing. It rather gives a comprehensive understanding of concepts and test techniques for all sorts of projects, including Agile. The syllabus discusses different types of software development lifecycles and methods not going into too much detail.

2. Glossary

This source helps a lot with unclear or unknown terms. There are 589 terms sorted alphabetically with definitions, synonyms, references, and versions. There is a Glossary app that you can download to your device or use the web version. You can use the basic search options or use advanced search to set filter criteria and change display options. And there’s an option to generate a pdf report, but you won’t necessarily have any use for it when preparing for the exam.

A tip to pass the exam would be to focus only on those terms which are highlighted before each chapter in the Syllabus.

3. Foundations of Software Testing: ISTQB Certification by Rex Black and others

Practical part of the exam is not explained in the Syllabus. That’s why I was reading Foundations of Software Testing co-authored by Rex Black, Dorothy Graham, and Erik P. W. M. Veenendaal, which expanded more on such topics as test design techniques. But the book is not limited to that only. All of the topics are presented in great detail. If some chapters of the Syllabus are hard to understand, Rex Black and authors do a really good job explaining all that in his book.

4. Udemy course

In addition to the Syllabus and Rex Black’s book, I took the course on Udemy which was roughly 5 hours of audio lectures. As mentioned above, this was the first bit of preparation that I did before diving into intense study. I’d recommend doing the same, as it sets a good foundation for understanding the topics once you start studying them more in depth.

5. Company corporate course or any other ISTQB accredited course

It was much easier for me to study in a group as I had people with whom I could communicate directly and ask questions. It was a big plus having a whole group of QAs to discuss different certification related topics, especially practical tasks. It’s much harder to comprehend the explanations from the book yourself than if a real professional explains it to you in a simple way. This part was very important for me. Many thanks to Maryna Shulga, our ISTQB angel.

6. Sample exam questions with answers

There are 3 sample tests with answers that are similar to the real exam. I advise everyone to look through all of them at least 2-3 days before the certification. These sample exam questions can be found here.

Also, a good resource with questions and answers that I used was the book ISTQB Foundation Sample Exam Questions by Chhavi Raj Dosaj.

Tips how to prepare for ISTQB exam: 

1. Try to memorize the information, not just passively reread it several times. It is a much more effective way to understand what you’ve learned than rereading. The best way is to reproduce what you’ve learned in small portions.  
   
2. First learn then test yourself, not vice versa. It is really important to first read the theory on ISTQB accredited sources like the Syllabus on ISTQB website and try to memorize the information and only then take tests. The best option would be to test yourself after each chapter. And only use reliable sources. If you use some online open resources with mock tests, it is not proof of knowledge. Some of them have outdated info and this way you’ll learn things that are no longer accurate. What is even worse is when these tests have the wrong answers, and you’ll learn from these bugged tests which may lead to you failing the exam. If you still want to test yourself with online tests, make sure that the information they provide is reliable. 
   
3. Take tests right after each learned section. Read the Syllabus (for chapters that are harder to understand, read the previously mentioned Foundations for Software Testing by sections. After each section take a test at the end of the chapter, check how well you know the topic and grade yourself out of 100%. Go back to the questions you know worse and test yourself again until you understand the topic.  
   
4. Take the full version of the test with a timer. After you have finished all the chapters, test yourself by taking a full exam of 40 sample questions on the ISTQB official website. There are 3 examples of questions to choose from with correct answers and explanations. This not only helps you test your knowledge, but also get familiar with the test itself. The exam sheet will have similar questions and the same format. Here it is important to have enough time, so as not to have problems with any formalities during the exam. Therefore, I would recommend taking all the full versions of the test (40 questions) considering a limited time frame. Train yourself at home to take the exam for strictly 75 minutes (for non-native speakers) or 60 minutes (for native speakers). 
   
5. On the last day before the certification read the Foundations of Software Testing section 7.2 Taking the Exam and make sure to have a good night’s sleep. 

A few more tips on how to pass the exam: 

1. Be sure to test the system before taking the test. On the day before the exam, log in and get acquainted with the system – get used to the interface, pay attention to the navigation, fonts, scrolling, clock, flag colors for different labels. This will cost you less stress on the exam, and you will be able to focus just on answering the questions.
   
2. Don’t waste too much time choosing the right answer.  Don’t focus too much on the questions – remember that each question costs 1 point no matter if it’s K1 or K3 complexity level. Use the orange flags if you are not sure of the answer, then go back to the same questions later.  
   
3. Carefully read the task statement. A common mistake during the exam is the lack of attention and desire to answer the question quickly. Therefore, it is crucial to pay attention to what is required in a particular task, namely, to read the task description carefully. Be careful not to confuse such statements as IS and NOT, CORRECT and INCORRECT, TRUE and FALSE, etc. Here banal inattention will cost you 1 point. 
   
4. Use scratch paper to work out the answers. If you are not sure about the answer, try the exclusion method. Write down on the paper the options you’re sure about and decide logically on the remaining ones. Also, you can write down the task number which you have skipped to answer later. This way you won’t waste time scrolling in the system when you get back to it. And finally, scratch paper is useful as an extra safety measure if for any reason there are problems with the Internet and all your answers in the system disappear. You can simply copy them from the paper.  

Conclusion

Speaking about benefits after the certification, I started to use more professional vocabulary at work. Also, it was a great way to solidify all my knowledge and give myself a good confidence boost.

But what is a full-stack developer? How are they positioned in a development team? What is the difference between a software engineer and a full-stack developer? Is one better than the other? And how do you become one? The list of questions can go on, but there’s no better way to find out about the ins and outs of the profession than to ask the person doing the job.

We have talked to Dimitrij Subashev, a Software Developer at Symphony Solutions Skopje office, about what becoming a full-stack developer entails, the intricacies and nuances of the job, and his experience in his career journey so far.

1. Hi Dimitrij! Could you please introduce yourself firstly a little bit?

Hello everyone. My name is Dimitrij Subashev and I’ve been a PHP developer for more than 5 years now with overall experience in the IT industry of over seven years. I’m one of the few people that founded the Skopje office. I have been working on many projects here at Symphony as a PHP (back end) developer and I have decided to try what full-stack is all about ?

2. What is a full-stack developer?   

For me, it is to be a ninja developer ? Just kidding! But it’s really a person who has excellent knowledge and skills in back-end (server software) and front-end (client software). Let’s look into this a little bit more just to simplify things:

• Front end (using JavaScript, jQuery, Angular, Vue, etc.)  
• Back end (using PHP, ASP, Python, or Node)  

And let’s not forget about:  

• database (using SQL, SQLite, or MongoDB).  

Working with full stack has some advantages:  

• You can provide help to all of the team members  
• You can reduce the cost of the project  
• You can make a prototype very rapidly  
• You can switch between front- and back-end development based on requirements  
• You can reduce the time used for team communication  

and disadvantages:  

• Being a full-stack developer is increasingly complex  
• The solution chosen can be unsuitable for the project  
• And also, it is barely possible to be up to date with all of front-end and back-end innovations.  

3. How did you decide that full-stack development is for you?  

I didn’t decide that full-stack is for me, I just decided to give it a try, because these days we hear that companies are looking for full-stack  developers. So, I told myself let’s see what will happen. I want to see if becoming a full-stack developer is really  the top of the iceberg for being  a developer. And if it’s really possible to be that good in both areas.

It’s not that I don’t want to become a ninja developer ?   

4. What skills helped you when studying?  

Well, let’s see.  As I’m a back-end developer and I want to become a full-stack (I need to learn some front-end),  so in this case, I have some start advantage, unlike a beginner.  Also,  I have some knowledge in JavaScript, jQuery,  HTML, and CSS,  so all I need to learn is some front-end framework. Angular, Vue,  React… I had in mind one of  these.

So basically,  every experience that you have or even a beginner’s knowledge that you have can help you  in this process.

5. What does a full-stack developer need to know now? 

Hmm…  This is a tough question.  Maybe I won’t answer correctly but things are changing so fast in the IT world ?  

What the roles and responsibilities of a full-stack developer will be, largely depends on the project. But if you’re looking for a general go-to list to get you started, according to the internet, these are the skills that you absolutely need to have:

• HTML/CSS  
• JavaScript  
• Git and GitHub   
• Back-end languages  
• Web architecture  
• HTTP and REST  
• Database storage  
• Basic design skills  
• NPM  
• Soft skills  

And I think this is a short list of skills. The real list will be much longer.  

All I can say is that he needs to be really, really, really good in back end and front end. And from what I can tell, this experience comes with years of dedicated and hard work… Ten years of experience and more. 

6. What program languages/technologies would you advise to focus on?

Even if you are a full-stack developer, you need to spend a lot of time (and when I say a lot, I mean it) on tracking all the new things that are introduced by those technologies that you are working with.

Everybody suggests using technologies that are working smoothly together. For example, Laravel Vue, Java Angular, React Express, etc. But this does not mean that you can’t combine Laravel Angular.  And, of course, you should be aware of the most popular programming languages and what they are.

7. Maybe you can recommend some resources where you study, for example, front-end development or DevOps and Cloud, or other technologies, or tools you used for each one when studying.

There are many ways to learn some new technology. The internet is one big library (or as we developers say, Google knows everything). You can use any other site, especially dedicated ones for these things like Udemy,  Pluralsight , or many others (for most of them you need to pay though).

I, for example, use Udemy,  YouTube has some free tutorials, and there are a lot of sites that can be found on Google that explain everything step by step. And not to forget that the best way to learn is from an experienced colleague that is willing to transfer his knowledge to you.

8. What to start with?  

This is hard to answer because there are a lot of influencing factors here.  Does the person have some kind of experience as a developer, is it back-end or front-end, or is he a beginner? If we are talking about beginners, then the first step will be to decide what they want more and what attracts them more, whether it’s front-end or back-end. Whatever they choose, then they need to start with something for beginners and continue gradually to advance. That will be the starting point for them.  And if  they have some developing experience then it’s much easier for them, they will just start  learning the other end that they are missing, whether is it a back-end or front-end.

9. What were the challenges you faced when learning new skills?  

There are a lot of challenges in this line of the job as an IT developer. I can’t single out any specific challenge while I’m learning or working but the choice to learn something new is a challenge by itself and it’s not an easy task. You won’t become a ninja overnight ?

By the way, my biggest challenge is free time because family time is very important to me.  

10. What advice to become a full-stack developer would you give to those who are starting out on this path?

As I mentioned before, the first step for beginners is to choose either the back-end or front-end and to continue from there on, or if the person has experience with either one, then just start with the beginner’s lessons for the opposite end.  

Try it, feel it and you will see how it goes for you. Either you like it or you don’t. 

11. Is being a full-stack engineer worth it? Maybe you can express your thoughts on being a full-stack vs half-stack developer?  

As I’m not a full-stack developer yet, I can’t answer this question. But on the other hand, if you are a good full-stack developer and you are doing your job right, then I guess you are worth twice as much to the company. In that case, I think, these people are a must.

12. How would you summarize your experience? Any advice?

Ok, finally the end! ? So, as a summary, what can I say more than I haven’t said so far?

Don’t be afraid and try something new. You never know, maybe you will like the new technologies so much that you will change your career path completely, start working in it and never look back.

Or who knows maybe you will be the next full-stack developer.  

Whatever you do, do what attracts you and what you like. Never spend time learning something that you don’t like just because someone said that it’s popular now. Now it’s in, tomorrow it’s out, and you are stuck with something that you don’t like. So, know what you want, keep your focus on the goal and upgrade yourself as much as you can.

These are the valuable insights that Dimitrij kindly shared with us. Now we all know a little bit more about how to become a full-stack developer. It’s a process that requires a lot of work and dedication. It takes time. A full-stack developer is a versatile engineer that never stops learning and is involved in all processes in a development team. This is a role not for the light of the heart of feeble-minded. Just like Dmitrij said – it makes you a ninja.

Take it with a grain of salt but one thing is certain. If you set out on this journey, it will be demanding and you’ll never stop working on your own skills and evolving, but the reward at the end will be worth it. Full-stack development is a stimulating and exhilarating lifelong pursuit that opens up a wide range of opportunities. It’s what companies want. And you, as a developer, have all the power to make it what you want it to be, build your own path and maximize your potential.