Aven Jeffrey

Apache Spark in 24 Hours, Sams Teach Yourself (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Pearson ITP
• Seitenzahl: 445
• Altersempfehlung: ab 18 Jahre
• Erscheinungstermin: 31. August 2016
• Englisch
• ISBN-13: 9780134445830
• Artikelnr.: 53053282

Autorenporträt

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.

Inhaltsangabe

PART I:  GETTING STARTED WITH APACHE SPARK
Hour 1:  Introducing Apache Spark    
Hour 2:  Understanding Hadoop    
Hour 3:  Installing Spark    
Hour 4:  Understanding the Spark Application Architecture    
Hour 5:  Deploying Spark in the Cloud    
PART II:  PROGRAMMING WITH APACHE SPARK
Hour 6:  Learning the Basics of Spark Programming with RDDs    
Hour 7:  Understanding MapReduce Concepts    
Hour 8:  Getting Started with Scala    
Hour 9:  Functional Programming with Python    
Hour 10:  Working with the Spark API (Transformations and Actions)    
Hour 11:  Using RDDs: Caching, Persistence, and Output    
Hour 12:  Advanced Spark Programming    
PART III:  EXTENSIONS TO SPARK
Hour 13:  Using SQL with Spark    
Hour 14:  Stream Processing with Spark    
Hour 15:  Getting Started with Spark and R    
Hour 16:  Machine Learning with Spark
Hour 17:  Introducing Sparkling Water (H20 and Spark)    
Hour 18:  Graph Processing with Spark    
Hour 19:  Using Spark with NoSQL Systems    
Hour 20:  Using Spark with Messaging Systems    
PART IV:  MANAGING SPARK
Hour 21:  Administering Spark    
Hour 22:  Monitoring Spark    
Hour 23:  Extending and Securing Spark    
Hour 24:  Improving Spark Performance    
Index