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Reactive Data Handling: Book Review

Reactive Data Handling from Manning Publications is a free guide for understanding and designing reactive architecture. Here is the review.

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It's a common challenge faced today for any company or organization that has a web presence—how to handle the sheer tonnage of information that comes in on a daily, hourly, or a by-minute basis. So many companies are turning to software that enables real-time analysis of performance, which has become necessary for survival in today's landscape of constant attempts to differentiate from competitors. 

That's where reactive architecture comes in—the ability to process, categorize, and analyze information quickly and efficiently, and without a complete collapse of the system. For seasoned architects, a guide like Manning Publications' free Reactive Data Handling might seem rudimentary. But Reactive Data Handling succeeds because it targets the relative newcomer but doesn't handle them with kid gloves. 

Reactive Data Handling is a free compilation from Miguel Bernhardt of five chapters from five different books. The topics include analyzing data streams (Streaming Data), fault tolerance and recovery (Reactive Design Patterns), designing your first reactive web application (Reactive Web Applications), an introduction to MLlib (Spark in Action), and managing a data center (Mesos in Action). The chapters are designed to give you a foundation on which you can design your own web application effectively. 

The good aspects are very simple. Each chapter starts off with a basic introduction. Terms are explained and defined. Examples from real life are often given. Then the level of rigor escalates. Within a few pages, the connections between those terms lead to new levels of knowledge, and then again in another few pages and again. 

Once the basic foundation is set, chapters are broken into sections that cover important parts of the subject area. For instance, the chapter about fault tolerance and recovery starts off with the simple component pattern, then moves to the error kernel pattern, and then to the let-it-crash pattern. Understanding deepens. Knowledge is attained in order to allow for the application of skills. 

The great is also very simple. The use of graphs to break up information both visually and in terms of messaging helps ease the reader into the information by providing an avenue for learning that is not strictly based on technical language. A paragraph, as often in the chapter about analyzing data streams, comes with a quick illustration, which makes the information easier to process. 

The negative about the guide is more about style than substance. The voices of the different authors are varied. Some come across as friendly and enthusiastic, which more have that "professor"-like tone and still others read in a very dry tone. It's the challenge of dealing with numerous authors who might be experts but have their own style and writing persona—some more friendly than others. 

And, as a reader, you might be wondering at times, "How does this chapter connect to the last one?" The information is all there but connections between the chapters and transitions could use some work. 

Still, Reactive Data Handling was an enjoyable read. The information was solid and elucidating, and the challenge of learning that much information was appealing. Much of the guide had an enthusiastic tone. It's worth downloading just for the chapter about designing your first reactive web application. It had an element of excitement to it and combined illustrations, text, code snippets, and block quotes to make for a very engaging read.

To download a free copy of Reactive Data Handling, visit Manning Publications.

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Topics:
big data ,book review ,reactive architecture ,information stream ,machine learning

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