Big Data Principles and Best Practices of Scalable Real-Time Data Systems
Nathan Marz, James Warren

✅ 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.

✅ 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. I

✅ 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 you will Learn:

✅ Introduction to big data system

✅ Real-time processing of web-scale data

✅ Tools like Hadoop, Cassandra, and Storm

✅ Extensions to traditional database skills