Also known as event stream processing (ESP), real-time data streaming, and complex event processing (CEP), stream processing is the continuous processing of real-time data directly as it is produced or received.

Structuring data as a stream of events isn’t new, but with the advent of open source projects like Apache Kafka and others, stream processing is finally coming of age.

As more organizations turn to real-time data, businesses from finance, government, and transportation, to travel, and health care are adopting event driven architectures to modernize their infrastructure and power their businesses at scale.

With this guide, you'll learn:

What is stream processing, event sourcing, and complex events?

How stream processing can make your data systems more flexible and less complex

can make your data systems more flexible and less complex How to solve hardships with data integration and data integrity using events and logs

How to build a solid data infrastructure and integrate databases using Apache Kafka

Real-life case studies: how Google Analytics, Twitter, and LinkedIn used stream processing

Putting event streams into practice

You'll also how these projects can help you reorient your database architecture around streams and materialized views. The benefits 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.

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