Data Analytics With Hadoop
An Introduction for Data Scientists
Samenvatting
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
Specificaties
Inhoudsopgave
Part 1: Introduction to Distributed Computing
1. The Age of the Data Product
-What Is a Data Product?
-Building Data Products at Scale with Hadoop
-The Data Science Pipeline and the Hadoop Ecosystem
-Conclusion
2. An Operating System for Big Data
-Basic Concepts
-Hadoop Architecture
-Working with a Distributed File System
-Working with Distributed Computation
-Submitting a MapReduce Job to YARN
-Conclusion
3. A Framework for Python and Hadoop Streaming
-Hadoop Streaming
-A Framework for MapReduce with Python
-Advanced MapReduce
-Conclusion
4. In-Memory Computing with Spark
-Spark Basics
-Interactive Spark Using PySpark
-Writing Spark Applications
-Conclusion
5. Distributed Analysis and Patterns
-Computing with Keys
-Design Patterns
-Toward Last-Mile Analytics
-Conclusion
Part 2: Workflows and Tools for Big Data Science
6. Data Mining and Warehousing
-Structured Data Queries with Hive
-HBase
-Conclusion
7. Data Ingestion
-Importing Relational Data with Sqoop
-Ingesting Streaming Data with Flume
-Conclusion
8. Analytics with Higher-Level APIs
-Pig
-Spark’s Higher-Level APIs
-Conclusion
9. Machine Learning
-Scalable Machine Learning with Spark
-Conclusion
10. Summary: Doing Distributed Data Science
-Data Product Lifecycle
-Machine Learning Lifecycle
-Conclusion
Appendix A: Creating a Hadoop Pseudo-Distributed Development Environment
Appendix B: Installing Hadoop Ecosystem Products
Index
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