It’s amazing how Java keeps being among the top development languages and platforms for so long. Hey, it’s probably older than some of the developers that are using it. 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 the latest Java technologies and worldwide software development trends that influence the platform. Here’s their selection. Enjoy.

Java 10 and Java 11

Let’s start the list of latest Java technologies with, well, Java. If you haven’t been living under a rock for the past year, you probably know that 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 we expect two more in 2018: Java 10 and Java 11. Java 10 will introduce the local variable type inference and a new garbage collector interface. These features are supposed to simplify writing Java applications. But Java technologies innovation doesn’t stop there. Java 10 has an improved JVM which is going to make

The Rise of Kotlin

Kotlin who, you say? Yes, it might not be the most popular of programming languages (not even in the top 20) and a new player among the top Java technologies, but its vigorous rise to fame put Kotlin on our list. According to RedMonk’s biannual review of the most popular programming languages, Kotlin has jumped from number 65 to number 27 in less than a year. This is certainly impressive but not surprising. First of all, 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. Second, 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 perf

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 is one of the latest Java technologies. 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 is the shift from microservices to the 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. 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.

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 than