文件名称：Big.Data.Principles.and.best.practices.of.scalable.realtime.data.systems.161
文件大小：6.79MB
文件格式：PDF
更新时间：2019-05-06 09:07:14
BigData Principles Best Practices
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 Chapter 1: A new paradigm for Big Data Part 1: Batch layer Chapter 2: Data model for Big Data Chapter 3: Data model for Big Data: Illustration Chapter 4: Data storage on the batch layer Chapter 5: Data storage on the batch layer: Illustration Chapter 6: Batch layer Chapter 7: Batch layer: Illustration Chapter 8: An example batch layer: Architecture and algorithms Chapter 9: An example batch layer: Implementation Part 2: Serving layer Chapter 10: Serving layer Chapter 11: Serving layer: Illustration Part 3: Speed layer Chapter 12: Realtime views Chapter 13: Realtime views: Illustration Chapter 14: Queuing and stream processing Chapter 15: Queuing and stream processing: Illustration Chapter 16: Micro-batch stream processing Chapter 17: Micro-batch stream processing: Illustration Chapter 18: Lambda Architecture in depth