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Big Data: Principles and Best Practices of Scalable Realtime Data Systems

by

Big Data: Principles and Best Practices of Scalable Realtime Data Systems Cover

 

Synopses & Reviews

Publisher Comments:

Summary

Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.

This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.

What's Inside

  • Introduction to big data systems
  • Real-time processing of web-scale data
  • Tools like Hadoop, Cassandra, and Storm
  • Extensions to traditional database skills

About the Authors

Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing.

Table of Contents

  1. A new paradigm for Big Data
  2. PART 1 BATCH LAYER
  3. Data model for Big Data
  4. Data model for Big Data: Illustration
  5. Data storage on the batch layer
  6. Data storage on the batch layer: Illustration
  7. Batch layer
  8. Batch layer: Illustration
  9. An example batch layer: Architecture and algorithms
  10. An example batch layer: Implementation
  11. PART 2 SERVING LAYER
  12. Serving layer
  13. Serving layer: Illustration
  14. PART 3 SPEED LAYER
  15. Realtime views
  16. Realtime views: Illustration
  17. Queuing and stream processing
  18. Queuing and stream processing: Illustration
  19. Micro-batch stream processing
  20. Micro-batch stream processing: Illustration
  21. Lambda Architecture in depth

Synopsis:

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

About the Author

Nathan Marz is an engineer at Twitter. He was previously Lead Engineer at BackType, a marketing intelligence company that was acquired by Twitter in July of 2011. He is the author of two major open source projects: Storm, a distributed realtime computation system, and Cascalog, a tool for processing data on Hadoop. He is a frequent speaker and writes a blog at nathanmarz.com.

James Warren is an analytics architect at Storm8 with a background in big data processing, machine learning and scientific computing.

Product Details

ISBN:
9781617290343
Subtitle:
Principles and best practices of scalable realtime data systems
Author:
Marz, Nathan
Author:
Warren, James
Publisher:
Manning Publications
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Subject:
Computers-Reference - General
Subject:
Big Data;CAP Theorem;Cassandra;Data science;Distributed systems;HBase;Hadoop;Hypertable;Kafka;Layered architecture;MapReduce;MongoDB;NoSQL;RabbitMQ;S4;Voldemort;Zookeeper;analytics;databases;partition;petabyte;replications;scalability;shard;terabyte,
Edition Description:
Print PDF
Publication Date:
20150510
Binding:
Paperback
Language:
English
Pages:
328
Dimensions:
9.25 x 7.38 in

Related Subjects

Business » Computers
Business » High Tech Management
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Applications
Computers and Internet » Database » Design
Computers and Internet » Database » General
Computers and Internet » Internet » Web » Web Programming
Computers and Internet » Software Engineering » Programming and Languages
Health and Self-Help » Self-Help » General

Big Data: Principles and Best Practices of Scalable Realtime Data Systems New Trade Paper
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Product details 328 pages Manning Publications - English 9781617290343 Reviews:
"Synopsis" by ,

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. As scale and demand increase, so does Complexity. Fortunately, scalability and simplicity are not mutually exclusive—rather than using some trendy technology, a different approach is needed. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.

Big Data shows how to build these systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy to understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to use them in practice, and how to deploy and operate them once they're built.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.

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