• Title: Making Sense of Stream Processing: The Philosophy Behind Apache Kafka and Scalable Stream Data Platforms
• Author(s) Martin Kleppmann
• Publisher: O'Reilly Media, Inc. (2016); eBook (Compliments of Confluent)
• Permission: Free eBook is complimented by Confluent
• Hardcover/Paperback: N/A
• eBook: HTML and PDF (182 pages)
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-1-491-93728-0
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Book Description

How can event streams help make your application more scalable, reliable, and maintainable? This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. Structuring data as a stream of events isn't new, but with the advent of open source projects such as Apache Kafka and Apache Samza, stream processing is finally coming of age.

Using several case studies, it explains how these projects can help you reorient your database architecture around streams and materialized views. The benefits of this approach include better data quality, faster queries through precomputed caches, and real-time user interfaces. Learn how to open up your data for richer analysis and make your applications more scalable and robust in the face of failures.

• Understand stream processing fundamentals and their similarities to event sourcing, CQRS, and complex event processing
• Learn how logs can make search indexes and caches easier to maintain
• Explore the integration of databases with event streams, using the new Bottled Water open source tool
• Turn your database architecture inside out by orienting it around streams and materialized views

• Bill Ott is a Digital VP in Booz Allen Hamilton's Strategic Innovation Group with responsibility over the Digital Interactive team.
• Jimmy Pham is an avid technologist who’s designed, developed, and managed large software solutions for major private and public customers.
• Haluk Saker is an experienced system/cloud architect, he leads Digital’s DevOps practice, microservices architecture, and numerous cloud platforms investments.

• Amazon (Kafka Streams in Action)

• Data Processing, Data Analysis and Data Mining
• Big Data and Data Stream
• Data Science
• Database Theory and Systems
• Parallel, Concurrent, and Distributed Computing and Programming

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