Principles and best practices of scalable realtime data systems
Nathan Marz and James Warren
MEAP Began: January 2012
Softbound print: September 2013 (est.) | 425 pages
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Table of Contents, MEAP Chapters & Resources
|Table of Contents||Resources|
1. A new paradigm for Big Data - FREE
2. Data model for Big Data - AVAILABLE
3. Data storage on the batch layer - AVAILABLE
4. MapReduce and Batch Processing - AVAILABLE
5. Batch layer: Abstraction and composition - AVAILABLE
6. Batch layer: Tying it all together - AVAILABLE
7. Serving layer
8. Speed layer: Scalability and fault-tolerance
9. Speed layer: Abstraction and composition
10. Incremental batch processing
11. Lambda architecture in-depth
12. Piping the system together
13. Future of NoSQL and Big Data processing
Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling big data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you just need to take a different approach. Big data systems use many machines working in parallel to store and process data, which introduces fundamental challenges unfamiliar to most developers.
Big Data teaches you 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 implement them in practice, and how to deploy and operate them once they're built.
Big Data shows you how to build the back-end for a real-time service called SuperWebAnalytics.com—our version of Google Analytics. As you read, you'll discover that many standard RDBMS practices become unwieldy with large-scale data. To handle the complexities of Big Data and distributed systems, you must drastically simplify your approach. This book introduces a general framework for thinking about big data, and then shows how to apply technologies like Hadoop, Thrift, and various NoSQL databases to build simple, robust, and efficient systems to handle it.
- Introduction to the concepts and technologies of Big Data
- Work with emerging tools like Hadoop, Cassandra, Thrift, and more
- Build on the skills you've learned using traditional databases
- Real-time processing of web-scale data
This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.
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.
About the Early Access Version
This Early Access version of Big Data enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online
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