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Apache Spark in 24 Hours, Sams Teach Yourself

Apache Spark in 24 Hours, Sams Teach Yourself

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Description

  • Copyright 2017
  • Dimensions: 7" x 9-1/8"
  • Pages: 445
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-444580-5
  • ISBN-13: 978-0-13-444580-9

Apache Spark is a fast, scalable, and flexible open source distributed processing engine for big data systems and is one of the most active open source big data projects to date. In just 24 lessons of one hour or less, Sams Teach Yourself Apache Spark in 24 Hours helps you build practical Big Data solutions that leverage Spark’s amazing speed, scalability, simplicity, and versatility.

This book’s straightforward, step-by-step approach shows you how to deploy, program, optimize, manage, integrate, and extend Spark–now, and for years to come. You’ll discover how to create powerful solutions encompassing cloud computing, real-time stream processing, machine learning, and more. Every lesson builds on what you’ve already learned, giving you a rock-solid foundation for real-world success.

Whether you are a data analyst, data engineer, data scientist, or data steward, learning Spark will help you to advance your career or embark on a new career in the booming area of Big Data.

Learn how to
• Discover what Apache Spark does and how it fits into the Big Data landscape
• Deploy and run Spark locally or in the cloud
• Interact with Spark from the shell
• Make the most of the Spark Cluster Architecture
• Develop Spark applications with Scala and functional Python
• Program with the Spark API, including transformations and actions
• Apply practical data engineering/analysis approaches designed for Spark
• Use Resilient Distributed Datasets (RDDs) for caching, persistence, and output
• Optimize Spark solution performance
• Use Spark with SQL (via Spark SQL) and with NoSQL (via Cassandra)
• Leverage cutting-edge functional programming techniques
• Extend Spark with streaming, R, and Sparkling Water
• Start building Spark-based machine learning and graph-processing applications
• Explore advanced messaging technologies, including Kafka
• Preview and prepare for Spark’s next generation of innovations

Instructions walk you through common questions, issues, and tasks; Q-and-As, Quizzes, and Exercises build and test your knowledge; "Did You Know?" tips offer insider advice and shortcuts; and "Watch Out!" alerts help you avoid pitfalls. By the time you're finished, you'll be comfortable using Apache Spark to solve a wide spectrum of Big Data problems.

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Table of Contents

Preface     xii
PART I:  GETTING STARTED WITH APACHE SPARK
Hour 1:  Introducing Apache Spark     1

What Is Spark?     1
What Sort of Applications Use Spark?     3
Programming Interfaces to Spark     3
Ways to Use Spark     5
Q&A     8
Workshop     8
Hour 2:  Understanding Hadoop     11
Hadoop and a Brief History of Big Data     11
Hadoop Explained     12
Introducing HDFS     13
Introducing YARN     19
Anatomy of a Hadoop Cluster     22
How Spark Works with Hadoop     24
Q&A     25
Workshop     25
Hour 3:  Installing Spark     27
Spark Deployment Modes     27
Preparing to Install Spark     28
Installing Spark in Standalone Mode     29
Exploring the Spark Install     38
Deploying Spark on Hadoop     39
Q&A     43
Workshop     43
Exercises     44
Hour 4:  Understanding the Spark Application Architecture     45
Anatomy of a Spark Application     45
Spark Driver     46
Spark Executors and Workers     48
Spark Master and Cluster Manager     49
Spark Applications Running on YARN     51
Local Mode     56
Q&A     59
Workshop     59
Hour 5:  Deploying Spark in the Cloud     61
Amazon Web Services Primer     61
Spark on EC2     64
Spark on EMR     73
Hosted Spark with Databricks     81
Q&A     89
Workshop     89

PART II:  PROGRAMMING WITH APACHE SPARK
Hour 6:  Learning the Basics of Spark Programming with RDDs     91

Introduction to RDDs     91
Loading Data into RDDs     93
Operations on RDDs     106
Types of RDDs     111
Q&A     113
Workshop     113
Hour 7:  Understanding MapReduce Concepts     115
MapReduce History and Background     115
Records and Key Value Pairs     117
MapReduce Explained     118
Word Count: The “Hello, World” of MapReduce     126
Q&A     135
Workshop     136
Hour 8:  Getting Started with Scala     137
Scala History and Background     137
Scala Basics     138
Object-Oriented Programming in Scala     153
Functional Programming in Scala     157
Spark Programming in Scala     160
Q&A     163
Workshop     163
Hour 9:  Functional Programming with Python     165
Python Overview     165
Data Structures and Serialization in Python     170
Python Functional Programming Basics     178
Interactive Programming Using IPython     183
Q&A     194
Workshop     194
Hour 10:  Working with the Spark API (Transformations and Actions)     197
RDDs and Data Sampling     197
Spark Transformations     199
Spark Actions     206
Key Value Pair Operations     211
Join Functions     219
Numerical RDD Operations     229
Q&A     232
Workshop     233
Hour 11:  Using RDDs: Caching, Persistence, and Output     235
RDD Storage Levels     235
Caching, Persistence, and Checkpointing     239
Saving RDD Output     247
Introduction to Alluxio (Tachyon)     254
Q&A     257
Workshop     258
Hour 12:  Advanced Spark Programming     259
Broadcast Variables     259
Accumulators     265
Partitioning and Repartitioning     270
Processing RDDs with External Programs     278
Q&A     280
Workshop     280

PART III:  EXTENSIONS TO SPARK
Hour 13:  Using SQL with Spark     283

Introduction to Spark SQL     283
Getting Started with Spark SQL DataFrames     294
Using Spark SQL DataFrames     305
Accessing Spark SQL     316
Q&A     321
Workshop     322
Hour 14:  Stream Processing with Spark     323
Introduction to Spark Streaming     323
Using DStreams     326
State Operations     335
Sliding Window Operations     337
Q&A     340
Workshop     340
Hour 15:  Getting Started with Spark and R     343
Introduction to R     343
Introducing SparkR     350
Using SparkR     355
Using SparkR with RStudio     358
Q&A     361
Workshop     361
Hour 16:  Machine Learning with Spark     363
Introduction to Machine Learning and MLlib     363
Classification Using Spark MLlib     367
Collaborative Filtering Using Spark MLlib     373
Clustering Using Spark MLlib     375
Q&A     378
Workshop     379
Hour 17:  Introducing Sparkling Water (H20 and Spark)     381
Introduction to H2O     381
Sparkling Water—H2O on Spark     387
Q&A     397
Workshop     397
Hour 18:  Graph Processing with Spark     399
Introduction to Graphs     399
Graph Processing in Spark     402
Introduction to GraphFrames     406
Q&A     414
Workshop     414
Hour 19:  Using Spark with NoSQL Systems     417
Introduction to NoSQL     417
Using Spark with HBase     419
Using Spark with Cassandra     425
Using Spark with DynamoDB and More     429
Q&A     431
Workshop     432
Hour 20:  Using Spark with Messaging Systems     433
Overview of Messaging Systems     433
Using Spark with Apache Kafka     435
Spark, MQTT, and the Internet of Things     443
Using Spark with Amazon Kinesis     446
Q&A     451
Workshop     451

PART IV:  MANAGING SPARK
Hour 21:  Administering Spark     453

Spark Configuration     453
Administering Spark Standalone     461
Administering Spark on YARN     471
Q&A     477
Workshop     478
Hour 22:  Monitoring Spark     479
Exploring the Spark Application UI     479
Spark History Server     488
Spark Metrics     490
Logging in Spark     492
Q&A     499
Workshop     499
Hour 23:  Extending and Securing Spark     501
Isolating Spark     501
Securing Spark Communication     504
Securing Spark with Kerberos     512
Q&A     517
Workshop     517
Hour 24:  Improving Spark Performance     519
Benchmarking Spark     519
Application Development Best Practices     526
Optimizing Partitions     534
Diagnosing Application Performance Issues     536
Q&A     540
Workshop     541
Index     543

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