Summary

In closing this chapter, we invite you to work your way through each of the Scala code-based examples in the following chapters. The rate at which Apache Spark has evolved is impressive, and important to note is the frequency of the releases. So even though, at the time of writing, Spark has reached 2.2, we are sure that you will be using a later version.

If you encounter problems, report them at www.stackoverflow.com and tag them accordingly; you'll receive feedback within minutes--the user community is very active. Another way of getting information and help is subscribing to the Apache Spark mailing list: user@apachespark.org.

By the end of this chapter, you should have a good idea what's waiting for you in this book. We've dedicated our effort to showing you practical examples that are, on the one hand, practical recipes to solve day-to-day problems, but on the other hand, also support you in understanding the details of things taking place behind the scenes. This is very important for writing good data products and a key differentiation from others.

The next chapter focuses on ApacheSparkSQL. We believe that this is one of the hottest topics that has been introduced to Apache Spark for two reasons.

First, SQL is a very old and established language for data processing. It was invented by IBM in the 1970s and soon will be nearly half a century old. However, what makes SQL different from other programming languages is that, in SQL, you don't declare how something is done but what should be achieved. This gives a lot of room for downstream optimizations.

This leads us to the second reason. As structured data processing continuously becomes the standard way of data analysis in Apache Spark, optimizers such as Tungsten and Catalyst play an important role; so important that we've dedicated two entire chapters to the topic. So stay tuned and enjoy!