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Cassandra: The Definitive Guideby Eben Hewitt
Synopses & Reviews
Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters.
Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you:
If you have lots of data — whether it's gigabytes or petabytes — Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject.
"Now you have the opportunity to learn about Hadoop from a master-not only of the technology, but also of common sense and plain talk." — Doug Cutting, Hadoop Founder, Yahoo!
The growing popularity of Apache Cassandra rests on this database's ability to handle very large data sets that include hundreds of terabytes. This hands-on guide provides the details and practical examples developers need to understand Cassandra's non-relational database design and how to take advantage of it in a production environment.
Organizations large and small are adopting Apache Hadoop to deal with huge application data sets, and this comprehensive resource provides the key for unlocking the wealth this data holds.\\\\n
The growing popularity of Apache Cassandra rests on this databases ability to handle very large data sets that include hundreds of terabytes. This hands-on guide provides the details and practical examples you need to understand Cassandras non-relational database design and how to take advantage of it in a production environment.
Author Eben Hewitt (Java SOA Cookbook) pays special attention to data modeling, and demonstrates Cassandras many advantages, including its high availability, eventual consistency model, and ability to scale easily. If youre a developer with a startup, youll learn how to future-proof your application by implementing Cassandra before your storage needs become critical. Join Twitter, Cisco, Digg, Reddit, and other data-intensive organizations that have come to rely on Cassandras NoSQL design. This book shows you how.
What could you do with data if scalability wasn't a problem? With this hands-on guide, you'll learn how Apache Cassandra handles hundreds of terabytes of data while remaining highly available across multiple data centers — capabilities that have attracted Facebook, Twitter, and other data-intensive companies. Cassandra: The Definitive Guide provides the technical details and practical examples you need to assess this database management system and put it to work in a production environment.
Author Eben Hewitt demonstrates the advantages of Cassandra's nonrelational design, and pays special attention to data modeling. If you're a developer, DBA, application architect, or manager looking to solve a database scaling issue or future-proof your application, this guide shows you how to harness Cassandra's speed and flexibility.
About the Author
Eben Hewitt is a Principal on the architecture team at a multi-billion dollar national retail company, where he has been focused on designing and building their Service Oriented Architecture. He has worked in IT for ten years, working on large-scale web and SOA integration projects, distributed software, and messaging systems. Hewitt is the author of four previous programming books, several industry articles, and is a contributor to the O'Reilly book 97 Things Every Software Architect Should Know, edited by Richard Monson-Haefel. He is a popular speaker at industry conferences and local user groups.
Table of Contents
Foreword; Preface; Administrative Notes; Whats in This Book?; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Chapter 1: Meet Hadoop; 1.1 Data!; 1.2 Data Storage and Analysis; 1.3 Comparison with Other Systems; 1.4 A Brief History of Hadoop; 1.5 The Apache Hadoop Project; Chapter 2: MapReduce; 2.1 A Weather Dataset; 2.2 Analyzing the Data with Unix Tools; 2.3 Analyzing the Data with Hadoop; 2.4 Scaling Out; 2.5 Hadoop Streaming; 2.6 Hadoop Pipes; Chapter 3: The Hadoop Distributed Filesystem; 3.1 The Design of HDFS; 3.2 HDFS Concepts; 3.3 The Command-Line Interface; 3.4 Hadoop Filesystems; 3.5 The Java Interface; 3.6 Data Flow; 3.7 Parallel Copying with distcp; 3.8 Hadoop Archives; Chapter 4: Hadoop I/O; 4.1 Data Integrity; 4.2 Compression; 4.3 Serialization; 4.4 File-Based Data Structures; Chapter 5: Developing a MapReduce Application; 5.1 The Configuration API; 5.2 Configuring the Development Environment; 5.3 Writing a Unit Test; 5.4 Running Locally on Test Data; 5.5 Running on a Cluster; 5.6 Tuning a Job; 5.7 MapReduce Workflows; Chapter 6: How MapReduce Works; 6.1 Anatomy of a MapReduce Job Run; 6.2 Failures; 6.3 Job Scheduling; 6.4 Shuffle and Sort; 6.5 Task Execution; Chapter 7: MapReduce Types and Formats; 7.1 MapReduce Types; 7.2 Input Formats; 7.3 Output Formats; Chapter 8: MapReduce Features; 8.1 Counters; 8.2 Sorting; 8.3 Joins; 8.4 Side Data Distribution; 8.5 MapReduce Library Classes; Chapter 9: Setting Up a Hadoop Cluster; 9.1 Cluster Specification; 9.2 Cluster Setup and Installation; 9.3 SSH Configuration; 9.4 Hadoop Configuration; 9.5 Post Install; 9.6 Benchmarking a Hadoop Cluster; 9.7 Hadoop in the Cloud; Chapter 10: Administering Hadoop; 10.1 HDFS; 10.2 Monitoring; 10.3 Maintenance; Chapter 11: Pig; 11.1 Installing and Running Pig; 11.2 An Example; 11.3 Comparison with Databases; 11.4 Pig Latin; 11.5 User-Defined Functions; 11.6 Data Processing Operators; 11.7 Pig in Practice; Chapter 12: HBase; 12.1 HBasics; 12.2 Concepts; 12.3 Installation; 12.4 Clients; 12.5 Example; 12.6 HBase Versus RDBMS; 12.7 Praxis; Chapter 13: ZooKeeper; 13.1 Installing and Running ZooKeeper; 13.2 An Example; 13.3 The ZooKeeper Service; 13.4 Building Applications with ZooKeeper; 13.5 ZooKeeper in Production; Chapter 14: Case Studies; 14.1 Hadoop Usage at Last.fm; 14.2 Hadoop and Hive at Facebook; 14.3 Nutch Search Engine; 14.4 Log Processing at Rackspace; 14.5 Cascading; 14.6 TeraByte Sort on Apache Hadoop; Installing Apache Hadoop; Prerequisites; Installation; Configuration; Clouderas Distribution for Hadoop; Prerequisites; Standalone Mode; Pseudo-Distributed Mode; Fully Distributed Mode; Hadoop-Related Packages; Preparing the NCDC Weather Data; Colophon;
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