Building Spark applications

Using Spark in an interactive mode with the Spark shell is very good for quick prototyping; however for developing applications, we need an IDE. The choices for the Spark IDE have come a long way since the days of Spark 1.0. One can use an array of the Spark IDEs for developing algorithms, data wrangling (that is, exploring data), and modeling analytics applications. As a general rule of thumb, iPython and Zeppelin are used for data exploration IDEs. The language of choice for iPython is Python and Scala/Java for Zeppelin. This is a general observation; all of them can handle the major languages; Scala, Java, Python, and SQL. For developing Scala and Java, the preferred IDE is Eclipse and IntelliJ. We will mostly use the Spark shell (and occasionally iPython) in this book, as our focus is data wrangling and understanding the Spark APIs. Of course, deploying Spark applications require compiling for Java and Scala.

Building the Spark jobs is a bit trickier than building a normal application as all dependencies have to be available on all the machines that are in your cluster.

In this chapter, we will first look at iPython and Eclipse, and then cover the process of building a Java and Scala Spark job with Maven, and learn to build the Spark jobs with a non-Maven aware build system. A reference website for building Spark is at http://spark.apache.org/docs/latest/building-spark.html.