本书作者：Bill Chambers、Matei Zaharia、Shrey Mehrotra，由O'Reilly Media出版社于2017年1月出版，全书共450页。这里提供的是本书的 Early Release 版本，正式版尚未出版，而且目前还没有完整的内容。由于这本书有Matei Zaharia参与编写，所有很值得一看。

通过本书将学习到以下的知识：

• 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

如果想及时了解Spark、Hadoop或者Hbase相关的文章，欢迎关注微信公共帐号：iteblog_hadoop

本书的章节

Chapter 1: What is Spark?
Chapter 2: A Gentle Introduction to Spark
Chapter 3: Advanced Spark
Chapter 4: Structured APIs Overview
Chapter 5: Basic Structured Operations
Chapter 6: Working with Different Types of Data
Chapter 7: Aggregations
Chapter 8: Joins
Chapter 9: Data Sources
Chapter 10: Spark SQL
Chapter 11: Datasets
Chapter 12: Low Level APIs Overview
Chapter 13: Basic RDD Operations
Chapter 14: Advanced RDD Operations
Chapter 15: Distributed Variables
Chapter 16: How Spark Runs on a Cluster
Chapter 17: Writing Spark Applications
Chapter 18: Deploying Spark
Chapter 19: Monitoring and Debugging
Chapter 20: Configuring, Optimizing, and Tuning
Chapter 21: Streaming Concepts
Chapter 22: What Makes Streaming in Spark Different
Chapter 23: Structured Streaming
Chapter 24: Operationalizing Structured Streaming
Chapter 25: Advanced Analytics and Machine Learning 
Chapter 26: Preprocessing Data
Chapter 27: Classification
Chapter 28: Regression
Chapter 29: Recommendation
Chapter 30: Clustering
Chapter 31: Graph Analysis
Chapter 32: Deep Learning 
Chapter 33: Ecosystem and Communit
Chapter 34: Spark Packages
Chapter 35: R on Spark

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