Romeo Kienzler 
Mastering Apache Spark 2.x — Second Edition [EPUB ebook] 

Advanced analytics on your Big Data with latest Apache Spark 2.x

• An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities.


• Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark.


• Master the art of real-time processing with the help of Apache Spark 2.x

If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.

• Examine Advanced Machine Learning and Deep Learning with MLlib, Spark ML, System ML, H2O and Deep Learning4J


• Study highly optimised unified batch and real-time data processing using Spark SQL and Structured Streaming


• Evaluate large-scale Graph Processing and Analysis using Graph X and Graph Frames


• Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud


• Understand internal details of cost based optimizers used in Catalyst, System ML and Graph Frames


• Learn how specific parameter settings affect overall performance of an Apache Spark cluster


• Leverage Scala, R and python for your data science projects

Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark’s functionality and implement your data flows and machine/deep learning programs on top of the platform.

The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x.

You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, System ML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying Data Frames and Datasets.

You will also learn about the updates on the APIs and how Data Frames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks.

This book is an extensive guide to Apache Spark modules and tools and shows how Spark’s functionality can be extended for real-time processing and storage with worked examples.