- Scala Quote

In last few years, Scala has observed steady rise and wide adoption by developers and practitioners, especially in the fields of data science and analytics. On the other hand, Apache Spark which is written in Scala is a fast and general engine for large-scale data processing. Spark's success is due to many factors: easy-to-use API, clean programming model, performance, and so on. Therefore, naturally, Spark has more support for Scala: more APIs are available for Scala compared to Python or Java; although, new Scala APIs are available before those for Java, Python, and R.

Now that before we start writing your data analytics program using Spark and Scala (part II), we will first get familiar with Scala's functional programming concepts, object oriented features and the Scala collection APIs in detail (part I). As a starting point, we will provide a brief introduction to Scala in this chapter. We will cover some basic aspects of Scala including it's history and purposes. Then we will see how to install Scala on different platforms including Windows, Linux, and Mac OS so that your data analytics programs can be written on your favourite editors and IDEs. Later in this chapter, we will provide a comparative analysis between Java and Scala. Finally, we will dive into Scala programming with some examples.

In a nutshell, the following topics will be covered:

• History and purposes of Scala
• Platforms and editors
• Installing and setting up Scala
• Scala: the scalable language
• Scala for Java programmers
• Scala for the beginners
• Summary