Mastering Apache Spark

About This Book

• Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan
• Evaluate how Cassandra and Hbase can be used for storage
• An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities

Who This Book Is For

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.

What You Will Learn

• Extend the tools available for processing and storage
• Examine clustering and classification using MLlib
• Discover Spark stream processing via Flume, HDFS
• Create a schema in Spark SQL, and learn how a Spark schema can be populated with data
• Study Spark based graph processing using Spark GraphX
• Combine Spark with H20 and deep learning and learn why it is useful
• Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra
• Use Apache Spark in the cloud with Databricks and AWS

In Detail

Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations.

This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment.

Table of Contents

Chapter 1: Apache Spark
Chapter 2: Apache Spark Mllib
Chapter 3: Apache Spark Streaming
Chapter 4: Apache Spark Sql
Chapter 5: Apache Spark Graphx
Chapter 6: Graph-Based Storage
Chapter 7: Extending Spark With H2O
Chapter 8: Spark Databricks
Chapter 9: Databricks Visualization