Spark in Action

Working with big data can be complex and challenging, in part

because of the multiple analysis frameworks and tools required.

Apache Spark is a big data processing framework perfect for analyzing

near-real-time streams and discovering historical patterns in batched

data sets. But Spark goes much further than other frameworks. By

including machine learning and graph processing capabilities, it makes

many specialized data processing platforms obsolete. Spark's unified

framework and programming model significantly lowers the initial

infrastructure investment, and Spark's core abstractions are intuitive for

most Scala, Java, and Python developers.

processing. It starts with an introduction to the Spark architecture and

ecosystem followed by a taste of Spark's command line interface.

Readers then discover the most fundamental concepts and abstractions

of Spark, particularly Resilient Distributed Datasets (RDDs) and the

basic data transformations that RDDs provide. The first part of the

book covers writing Spark applications using the the core APIs.

Readers also learn how to work with structured data using Spark SQL,

how to process near-real time data with Spark Streaming, how to apply

machine learning algorithms with Spark MLlib, how to apply graph

algorithms on graph-shaped data using Spark GraphX, and an

introduction to Spark clustering.

Readers should be familiar with Java, Scala, or Python. No knowledge of

Spark or streaming operations is assumed, but some acquaintance with

machine learning is helpful.

Apache Spark is a big data processing framework perfect for analyzing

near-real-time streams and discovering historical patterns in batched data

sets. Spark also offers machine learning and graph processing capabilities.