Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.
Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.
Understand core concepts behind Hadoop and cluster computing
Use design patterns and parallel analytical algorithms to create distributed data analysis jobs
Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase
Use Sqoop and Apache Flume to ingest data from relational databases
Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames
Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib
Table of Contents
Part I. Introduction to Distributed Computing
Chapter 1. The Age of the Data Product
Chapter 2. An Operating System for Big Data
Chapter 3. A Framework for Python and Hadoop Streaming
Chapter 4. In-Memory Computing with Spark
Chapter 5. Distributed Analysis and Patterns
Part II. Workflows and Tools for Big Data Science
Chapter 6. Data Mining and Warehousing
Chapter 7. Data Ingestion
Chapter 8. Analytics with Higher-Level APIs
Chapter 9. Machine Learning
Chapter 10. Summary: Doing Distributed Data Science
Appendix A. Creating a Hadoop Pseudo-Distributed Development Environment
Appendix B. Installing Hadoop Ecosystem Products
