Scala for Enterprise Data Is Growing Steadily & Here’s Why
In 2020, companies are collecting more data than ever before. Most corporations now track user events, employee actions, demographic data, aggregate sales figures, inventory updates, and more all in data stores in the cloud.
When it comes to parsing, monitoring, and analyzing that data in real-time, increasingly companies are turning to custom-developed big data software. Among the leading languages for writing that software is Scala, a well-established language, based on Java, with features and syntax built for data processing.
In this article, we’ll examine what scala is, why companies choose it, and what it means for future data applications.
Scala = Java Virtual Machine + Functional Programming
Scala is part of a cohort of programming languages that are built atop the Java Virtual Machine. This means that although Scala has a different syntax than the Java language, it can still compile down to the same bytecode as Java. Thus, it can run on the same engine Java uses, complete with Java’s ubiquity, security, and performance benefits.
So, what’s different about Scala and why choose it over Java? Put simply, Scala introduces a cleaner syntax and more features than the Java language. Specifically, Scala provides support for first-class functions and closures as part of the language. This opens Scala up to use functional programming paradigms – something that’s not fully possible with Java.
Moreover, Scala code is more concise and clear than Java. Its syntax includes less boilerplate and is easier for developers to understand while also taking fewer lines to accomplish the same task.
That said, you still get all the benefits of the Java ecosystem with Scala. Scala programs can import Java libraries and use existing code written in Java. This interoperability is a key feature of Scala and one reason why enterprise companies, often with large existing Java codebases, choose Scala for their data processing.
Pattern Matching & Big Data Analysis
Scala’s largest growth area is enterprise data processing. This is because Scala works well within other tools for data matching and analysis like Apache Spark. Spark is an interface for programming entire clusters of computers, so the code you right needs to have high concurrency support – a specialty of the Java Virtual Machine and a benefit of Scala’s functional programming.
Writing a Scala program that runs with Spark means you can consume and transform data from many sources in parallel. This includes access to streaming data, SQL, machine learning, and graph processing via Spark’s integrations.
Established History & Community
While Scala may sound like an upstart, modern language, it’s actually over 15 years old. It has a well-established ecosystem and community of users. This community support is especially strong for big data applications. What’s more, you’ll also still have access to all Java libraries when you’re programming in Scala with Java’s decades of enterprise use.
Scala itself is in use at some of the top companies and startups in the world. Morgan Stanley, Verizon, Apple, and The New York Times use Scala for their enterprise data processing needs. Top startups like LinkedIn, Twitter, Netflix, and Airbnb also use Scala to consume and process incoming user data.
Scala’s Growth Is an Indicator of the Future of Big Data
As more companies collect more data, tools that help process and analyze that data become increasingly important. Scala’s growth indicates that companies are seriously thinking about and using concurrency and parallelism to process millions of data points, driving insights in nearly every industry.
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