Apache Spark in 24 Hours, Sams Teach Yourself

  • ISBN13:


  • ISBN10:


  • Edition: 1st
  • Format: Paperback
  • Copyright: 8/17/2016
  • Publisher: Sams Publishing

Note: Supplemental materials are not guaranteed with Rental or Used book purchases.

Purchase Benefits

  • Free Shipping On Orders Over $59!
    Your order must be $59 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • Get Rewarded for Ordering Your Textbooks! Enroll Now
List Price: $44.99 Save up to $15.75
  • Rent Book $29.24
    Add to Cart Free Shipping


Supplemental Materials

What is included with this book?

  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
  • The Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.


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.

Author Biography

Jeffrey Aven is a big data consultant and instructor based in Melbourne, Australia. Jeff has an extensive background in data management and several years of experience consulting and teaching in the areas of Hadoop, HBase, Spark, and other big data ecosystem technologies. Jeff has won accolades as a big data instructor and is also an accomplished consultant who has been involved in several high-profile, enterprise-scale big data implementations across different industries in the region.

Table of Contents

Hour 1. Introducing Apache Spark
Hour 2. Spark and the Big Data Landscape
Hour 3. Deploying Spark
Hour 4. Spark Cluster Architecture
Hour 5. Spark Programming Basics
Hour 6. Scala Programming Primer
Hour 7. Functional Python Programming
Hour 8. MapReduce Revisited
Hour 9. The Spark API (Transformations and Actions)
Hour 10. RDDs: Caching, Persistence and Output
Hour 11. Advanced Spark Programming
Hour 12. Using SQL with Spark
Hour 13. Common Processing Patterns in Spark
Hour 14. Spark Streaming
Hour 15. Spark and R
Hour 16. Machine Learning in Spark
Hour 17. Sparkling Water (H20 and Spark)
Hour 18. Managing Spark
Hour 19. Extending Spark
Hour 20. Improving Spark Performance
Hour 21. Spark in the Cloud
Hour 22. Spark and NoSQL using Apache Cassandra
Hour 23. Spark and Message Queues
Hour 24. The Future for Spark

Rewards Program

Write a Review