Spark: The Definitive Guide
Big data processing made simple
Publisher: O'Reilly Media
Release Date: January 2017
Pages: 450
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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
Table of Contents
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Chapter 1 What is Spark?
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Chapter 2 A Gentle Introduction to Spark
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Chapter 3 Advanced Spark
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Chapter 4 Structured APIs Overview
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Chapter 5 Basic Structured Operations
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Chapter 6 Working with Different Types of Data
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Chapter 7 Aggregations
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Chapter 8 Joins
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Chapter 9 Data Sources
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Chapter 10 Spark SQL
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Chapter 11 Datasets
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Chapter 12 Low Level APIs Overview
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Chapter 13 Basic RDD Operations
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Chapter 14 Advanced RDD Operations
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Chapter 15 Distributed Variables
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Chapter 16 How Spark Runs on a Cluster
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Chapter 17 Writing Spark Applications
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Chapter 18 Deploying Spark
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Chapter 19 Monitoring and Debugging
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Chapter 20 Configuring, Optimizing, and Tuning
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Chapter 21 Streaming Concepts
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Chapter 22 What Makes Streaming in Spark Different
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Chapter 23 Structured Streaming
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Chapter 24 Operationalizing Structured Streaming
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Chapter 25 Advanced Analytics and Machine Learning
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Chapter 26 Preprocessing Data
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Chapter 27 Classification
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Chapter 28 Regression
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Chapter 29 Recommendation
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Chapter 30 Clustering
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Chapter 31 Graph Analysis
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Chapter 32 Deep Learning
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Chapter 33 Ecosystem and Communit
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Chapter 34 Spark Packages
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Chapter 35 R on Spark