Spark: The Definitive Guide: Big data processing made simple
by: Bill Chambers – Matei Zaharia
ISBN-10: 1491912219
ISBN-13: 9781491912218
Edition 版次: 1
Publication Date 出版日期: 2017-10-25
Print Length 页数: 450
Book Description
By finelybook
Learn how to use,deploy,and maintain Apache Spark with this comprehensive guide,written by the creators of this open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0,authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections,each with unique goals.
You’ll explore the basic operations and common functions of Spark’s structured APIs,as well as Structured Streaming,a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring,tuning,and debugging Spark,and explore machine learning techniques and scenarios for employing MLlib,Spark’s scalable machine learning library.
Get a gentle overview of big data and Spark
Learn about DataFrames,SQL,and Datasets—Spark’s core APIs—through worked examples
Dive into Spark’s low-level APIs,RDDs,and execution of SQL and DataFrames
Understand how Spark runs on a cluster
Debug,monitor,and tune Spark clusters and applications
Learn the power of Spark’s Structured Streaming and MLlib for machine learning tasks
Explore the wider Spark ecosystem,including SparkR and Graph Analysis
Examine Spark deployment,including coverage of Spark in the Cloud
Contents
Chapter 1. A Gentle Introduction to Spark
Chapter 2. Structured API Overview
Chapter 3. Basic Structured Operations
Chapter 4. Working with Different Types of Data
Chapter 5. Aggregations
Chapter 6. Joins
Chapter 7. Data Sources
Chapter 8. Spark SQL
Chapter 9. Datasets
Chapter 10. Low Level API Overview
Chapter 11. Basic RDD Operations
Chapter 12. Advanced RDDs Operations
Chapter 13. Distributed Variables
Chapter 14. Advanced Analytics and Machine Learning
Chapter 15. Preprocessing and Feature Engineering
Chapter 16. Preprocessing
Chapter 17. Classification
Chapter 18. Regression
Chapter 19. Recommendation
Chapter 20. Clustering
Chapter 21. Graph Analysis
Chapter 22. Deep Learning
