Chapter 1. A First Taste and What's New in Apache Spark V2

Apache Spark is a distributed and highly scalable in-memory data analytics system, providing you with the ability to develop applications in Java, Scala, and Python, as well as languages such as R. It has one of the highest contribution/involvement rates among the Apache top-level projects at this time. Apache systems, such as Mahout, now use it as a processing engine instead of MapReduce. It is also possible to use a Hive context to have the Spark applications process data directly to and from Apache Hive.

Initially, Apache Spark provided four main submodules--SQL, MLlib, GraphX, and Streaming. They will all be explained in their own chapters, but a simple overview would be useful here. The modules are interoperable, so data can be passed between them. For instance, streamed data can be passed to SQL and a temporary table can be created. Since version 1.6.0, MLlib has a sibling called SparkML with a different API, which we will cover in later chapters.

The following figure explains how this book will address Apache Spark and its modules:

The top two rows show Apache Spark and its submodules. Wherever possible, we will try to illustrate by giving an example of how the functionality may be extended using extra tools.

Note

We infer that Spark is an in-memory processing system. When used at scale (it cannot exist alone), the data must reside somewhere. It will probably be used along with the Hadoop toolset and the associated ecosystem.

Luckily, Hadoop stack providers, such as IBM and Hortonworks, provide you with an open data platform, a Hadoop stack, and cluster manager, which integrates with Apache Spark, Hadoop, and most of the current stable toolset fully based on open source.

During this book, we will use the Hortonworks Data Platform (HDP^®) Sandbox 2.6.

You can use an alternative configuration, but we find that the open data platform provides most of the tools that we need and automates the configuration, leaving us more time for development.

In the following sections, we will cover each of the components mentioned earlier in more detail before we dive into the material starting in the next chapter:

• Spark Machine Learning
• Spark Streaming
• Spark SQL
• Spark Graph Processing
• Extended Ecosystem
• Updates in Apache Spark
• Cluster design
• Cloud-based deployments
• Performance parameters