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
