Mastering Apache Spark

Gain expertise in processing and storing data by using advanced techniques with Apache Spark

Mastering Apache Spark

This ebook is included in a Mapt subscription
Mike Frampton

2 customer reviews
Gain expertise in processing and storing data by using advanced techniques with Apache Spark
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Book Details

ISBN 139781783987146
Paperback318 pages

Book Description

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
Overview
Cluster design
Cluster management
Performance
Cloud
Summary
Chapter 2: Apache Spark MLlib
The environment configuration
Classification with Naïve Bayes
Clustering with K-Means
ANN – Artificial Neural Networks
Summary
Chapter 3: Apache Spark Streaming
Overview
Errors and recovery
Streaming sources
Summary
Chapter 4: Apache Spark SQL
The SQL context
Importing and saving data
DataFrames
Using SQL
User-defined functions
Using Hive
Summary
Chapter 5: Apache Spark GraphX
Overview
GraphX coding
Mazerunner for Neo4j
Summary
Chapter 6: Graph-based Storage
Titan
TinkerPop
Installing Titan
Titan with HBase
Titan with Cassandra
Accessing Titan with Spark
Summary
Chapter 7: Extending Spark with H2O
Overview
The processing environment
Installing H2O
The build environment
Architecture
Sourcing the data
Data Quality
Performance tuning
Deep learning
H2O Flow
Summary
Chapter 8: Spark Databricks
Overview
Installing Databricks
AWS billing
Databricks menus
Account management
Cluster management
Notebooks and folders
Jobs and libraries
Development environments
Databricks tables
The DbUtils package
Summary
Chapter 9: Databricks Visualization
Data visualization
REST interface
Moving data
Further reading
Summary

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

Authors

Table of Contents

Chapter 1: Apache Spark
Overview
Cluster design
Cluster management
Performance
Cloud
Summary
Chapter 2: Apache Spark MLlib
The environment configuration
Classification with Naïve Bayes
Clustering with K-Means
ANN – Artificial Neural Networks
Summary
Chapter 3: Apache Spark Streaming
Overview
Errors and recovery
Streaming sources
Summary
Chapter 4: Apache Spark SQL
The SQL context
Importing and saving data
DataFrames
Using SQL
User-defined functions
Using Hive
Summary
Chapter 5: Apache Spark GraphX
Overview
GraphX coding
Mazerunner for Neo4j
Summary
Chapter 6: Graph-based Storage
Titan
TinkerPop
Installing Titan
Titan with HBase
Titan with Cassandra
Accessing Titan with Spark
Summary
Chapter 7: Extending Spark with H2O
Overview
The processing environment
Installing H2O
The build environment
Architecture
Sourcing the data
Data Quality
Performance tuning
Deep learning
H2O Flow
Summary
Chapter 8: Spark Databricks
Overview
Installing Databricks
AWS billing
Databricks menus
Account management
Cluster management
Notebooks and folders
Jobs and libraries
Development environments
Databricks tables
The DbUtils package
Summary
Chapter 9: Databricks Visualization
Data visualization
REST interface
Moving data
Further reading
Summary

Book Details

ISBN 139781783987146
Paperback318 pages
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