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

De: Nathan Marz, James Warren

Narrado por: Mark Thomas, Chris Penick

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.

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.

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.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2015 Manning Publications (P)2015 Manning Publications

It’s a good book, but audio doesn’t help things

It’s one of those books that you should read, not listen to. The images as well as the complex ideas need to be digested in written, not auditory, format.

Microsoft stack looking at Big Data

This was a great book about the Lambda architecture. I’m coming from a Microsoft stack (sql server etc.) background and I found this a great explanation of an alternative way to store, compute and serve the data without all the traditional Sql Server tooling.

Definitely got me excited to explore alternate data pipeline architectures outside of the Microsoft world.