Implement streaming systems to handling time-sensitive problems

We are happy to announce the availability of Fast Data Architectures For Streaming Applications, a new O'Reilly report authored by the architect for fast data products at Lightbend, Dean Wampler (@deanwampler). In this report he examines the rise of streaming systems for handling time-sensitive problems —like detecting fraudulent financial activity as it happens. You’ll explore the characteristics of fast data architectures, along with several open source tools for implementing them.

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About this mini-book

Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? In this report, author Dean Wampler examines the rise of streaming systems for handling time-sensitive problems—like detecting fraudulent financial activity as it happens. You’ll explore the characteristics of fast data architectures, along with several open source tools for implementing them.

Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention.

With this report, you will:

Learn step-by-step how a basic fast data architecture works

Understand why event logs are the core abstraction for streaming architectures, while message queues are the core integration tool

Use methods for analyzing infinite data sets, where you don’t have all the data and never will

Take a tour of open source streaming engines, and discover which ones work best for different use cases

Get recommendations for making real-world streaming system responsive, resilient, elastic, and message driven

Explore an example streaming application for the IoT: telemetry ingestion and anomaly detection for home automation systems

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