Most developers who grapple with big data are data engineers, data scientists, or machine learning engineers. This book is aimed at those professionals who are looking to use Spark to scale their applications to handle massive amounts of data. In particular, data engineers will learn how to use Spark's Structured APIs to perform complex data exploration and analysis on both batch and streaming data; use Spark SQL for interactive queries; use Spark's built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks; and build reliable data lakes with Spark and the open source Delta Lake table format. For data scientists and machine learning engineers, Spark's MLlib library offers many common algorithms to build distributed machine learning models. We will cover how to build pipelines with MLlib, best practices for distributed machine learning, how to use Spark to scale single-node models, and how to manage and deploy these models using the open source library MLflow.

Apache Spark, the open-source cluster computing framework, will soon see a major update with the upcoming release of Spark 2.0. This update promises to be faster than Spark 1.6, thanks to a run-time compiler that generates optimized bytecode. It also promises to be easier for developers to use, with streamlined APIs and a more complete SQL implementation. Spark 2.0 will also include a new "structured streaming" API, which will allow developers to write algorithm for streaming data without having to worry about the fact that streaming data is always incomplete; algorithms written for complete DataFrame objects will work for streams as well. This update also includes some news for R users.