Sheer folds of silk converging

Scala is a highly scalable general purpose programming language that combines aspects of both object-oriented and functional programming. It’s become increasingly important in the world of data science, rivaling more established languages like Java and Python. One of the main drivers of Scala’s rise to prominence has been the explosive growth of Apache Spark (which is written in Scala), giving Scala a well-earned reputation as a powerful language for data processing, machine learning, and streaming analytics.

What is Scala, and what makes it so well suited to handling big data? In this article we’re going to look at what sets Scala apart as a programming language, why it’s becoming increasingly important to data scientists, and what skills you should look for in a Scala developer.

Powerful and General Purpose

Scala started out as an effort to address some of Java’s perceived shortcomings, namely its verbosity. While Java’s basic syntax may be easy to learn, it can take many lines of code to express basic ideas. Scala, by contrast, is designed to be much more concise and expressive. This does give it a steeper learning curve than Java, but for many developers the trade off is well worth it.

Still, the Java legacy is clear in many of Scala’s attributes, from its strong OOP support, to its curly brace syntax, to its high level of interoperability with Java libraries. What’s more, Scala’s source code is written to be compiled to Java bytecode and then run on the Java Virtual Machine, making it highly portable and safe. This gives Scala a wide-range of potential applications. Its Java compatibility makes it well suited to developing for Android, and its ability to compile to Javascript means Scala can even be used to write web apps. If you’re an object-oriented programmer who has no interest in learning functional programming, you can still pick up Scala and take advantage of Java’s many advantages (its rich libraries and the Java Virtual Machine) all while writing less boilerplate. If you’re looking to hire a Scala programmer, you can create a job post on Upwork anytime.

Combining Functional and Object-Oriented Programming

One of Scala’s major advantages is its support for both object-oriented and functional programming. Both approaches aim to create readable, bug-free code, but they go about it in very different ways. Where object-oriented programming combines data structures with the actions you want to perform on them, functional programming keeps both separate.

Each approach has its advantages. For many people, the object-oriented paradigm makes intuitive sense, and combining behaviors with the data structures they’ll interact with can make it easy to figure out what’s going on in an unfamiliar codebase. At the same time, functional programming’s preference for cleanly separated and immutable data structures and discrete behaviors often allows you to do more with less code.

As we’ve said, Scala is a fully fledged OOP language, and it’s possible to write highly elegant and expressive programs without even touching its functional attributes. But for those who are curious about functional programming, Scala provides a rich set of collection operations (like map and reduce), higher-order functions, and a strong static typing system.

About that static typing system: Where many other modern programming languages are dynamically typed, Scala checks types at compile time, meaning that many trivial but costly bugs can be caught at compile time rather than in production. At the same time, Scala has a highly sophisticated type system (more so than Python), meaning that developers can enjoy the security of compile-time type-checking without having to worry about specifying every type every time.

Scala and Spark

One of Scala’s biggest attractions is its close relationship with the cluster-computing framework Spark. Like Hadoop, Spark is used for processing massive datasets across clusters of commodity hardware. But where Hadoop relies on the venerable MapReduce paradigm, Spark uses a different process that copies most of its operations into the system’s RAM. By handling these operations “in memory,” Spark can achieve much higher speeds than MapReduce. This speed gain makes Spark uniquely well suited to streaming data analytics.

Another difference: Where Hadoop is mostly written in Java, Spark is written in Scala. While Spark includes APIs for working in Java, Python, and R, there are definite advantages to working in its original language, such as the ability to access new features that haven’t been ported over to other languages. Additionally, translating between different languages and environments can lead to bugs and slowdowns, which gives Scala an engineering advantage relative to Python or Java.

That said, the best language to use depends largely on the problems you’re trying to solve. While Spark comes with the MLlib machine learning library, there are still major advantages to going with Python’s high-quality, mature libraries. for machine learning and statistical analysis problems.

What to Look for in a Scala Developer

As with any developer role, the exact skills and experience you want will depend on your project and business goals. When looking for a Scala developer, it’s important to not only gauge their skills with the language, but also whether they’re able to learn quickly and build resilient systems. Experience with testing and program design are invaluable. Beyond those skills, here are some specific technologies and paradigms to look for in a Scala developer:

  • Object-oriented programming
  • The Java Virtual Machine
  • Tools of statistical analysis
  • Distributed file storage systems (like HDFS)
  • SQL and relational database management systems

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