Streaming Architecture: New Designs Using Apache Kafka and MapR Streams

by Ted Dunning, Ellen Friedman

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, youíll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm.

Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases.

Ideal for developers and non-technical people alike, this book describes:

Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layer New messaging technologies, including Apache Kafka and MapR Streams, with links to sample code Technology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache Apex How stream-based architectures are helpful to support microservices Specific use cases such as fraud detection and geo-distributed data streams

Genres: Programming

120 pages, Paperback

Published January 1, 2017

Reviews

[Sebastian Gebski · Rating 2 out of 5]

No API description, No code samples = No Go.

Not that bad as a high-level arch overview, but the knowledge it provides is sufficient to create a diagram or presentation, not a working, real-life system. Trivialized sample scenarios (based mainly on textual descriptions ...) don't help much either. Definitely not worth its price. The only actual benefit I've perceived is the high-level comparison - Kafka VS MapR Streams.

[Kevin Lai]

In 2016 we saw the rise of streaming applications and Kafka has evolved a lot since then, and there are better books that dive deeper into data streaming and event-driven architecture. But for a book I can flip through in 2 hours, this book serves as a good starting point to build streaming applications, and a lot of the arguments and reasoning are still valid, so I still recommend it.

[Krishnaprasad Shivdasan · Rating 3 out of 5]

It provides a very high level overview of streaming architecture and some details around Kafka and MapR streams, enough to pique your interest in digging deeper into the concept of streaming architecture and frameworks that enable it.

[Purushottam · Rating 3 out of 5]

Nice introduction

Good preview into stream based architecture; not much from code perspective ; expected to be in-depth analysis of Kafka and mapr turned out to be just a introduction

[Avraam Mavridis · Rating 2 out of 5]

Honestly, nothing much of a value, a high level overview of the benefits of streaming architectures, a comparison of various tools and that's it. I was expecting real world scenarios and examples...

[Kirill · Rating 4 out of 5]

A great introduction to streaming with minimum self-advertisement.

[Subhadip Mitra · Rating 1 out of 5]

Poor, not detailed enough.
Avoid. You will find better resources on the internet.

[Elliot N Franford · Rating 5 out of 5]

Great overview with enough meat to get you started

Provides a good look into streaming architectures and gives you a clear understanding of using and adopting streaming. Great book!

[Tuấn Anh Nguyễn · Rating 3 out of 5]

Very short. Good at highlighting the benefits of a streaming architecture, and some of Kafka's downsides, but contains a little bit too much of hand-wavy MapR Streams evangelization.

[Kimberly Grommes · Rating 3 out of 5]

This book is a high level overview only and glossed over many details that would make it more useful. It is quite repetitive as well so page count could remain same while providing more detail. The Kafka part is used mostly as introduction for the supposedly better functionality of MapR Streams.

[Alex Ott · Rating 3 out of 5]

Very MapR oriented...

[Dmitry · Rating 4 out of 5]

The book can be easily read in one day, it gives just a brief introduction into streaming architecture idea, Kafka and MapR, no code samples, just plain text