As you might already have understood from the previous chapters, Apache Spark is currently in transition from RDD-based data processing to a more structured one, backed by DataFrames and Datasets in order to let Catalyst and Tungsten kick in for performance optimizations. This means that the community currently uses a double-tracked approach. While the unstructured APIs are still supported--they haven't even been marked as deprecated yet ,and it is questionable if they ever will--a new set of structured APIs has been introduced for various components with Apache Spark V 2.0, and this is also true for Spark Streaming. Structured Steaming was marked stable in Apache Spark V 2.2. Note that, as of Apache Spark V 2.1 when we started writing this chapter, Structured Streaming is was marked as alpha. This is another example of the extreme pace at which Apache...
-
Book Overview & Buying
-
Table Of Contents
Mastering Apache Spark 2.x - Second Edition
By :
Mastering Apache Spark 2.x
By:
Overview of this book
Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform.
The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud.
By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (15 chapters)
Preface
Free Chapter
A First Taste and What’s New in Apache Spark V2
Apache Spark SQL
The Catalyst Optimizer
Project Tungsten
Apache Spark Streaming
Structured Streaming
Apache Spark MLlib
Apache SparkML
Apache SystemML
Deep Learning on Apache Spark with DeepLearning4j and H2O
Apache Spark GraphX
Apache Spark GraphFrames
Apache Spark with Jupyter Notebooks on IBM DataScience Experience
