Making Sense of Stream Processing

The Philosophy Behind Apache Kafka and Scalable Stream Data Platforms

Making Sense of Stream Processing

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How can event streams help make your application more scalable, reliable, and maintainable? In this report, O’Reilly author Martin Kleppmann 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, Kleppmann 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

Martin Kleppmann has worked on large-scale data infrastructure at several internet companies, including LinkedIn, which acquired his previous startup Rapportive. He is now an academic researcher at the University of Cambridge. Martin is an active contributor to open source, as committer at the Apache Software Foundation and creator of Bottled Water (an integration of PostgreSQL and Kafka).

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Martin Kleppmann

Martin Kleppmann is a researcher and engineer in the area of distributed systems, databases and security at the University of Cambridge, UK. He previously co-founded two startups, including Rapportive, which was acquired by LinkedIn in 2012. Through working on large-scale production data infrastructure, experimental research systems, and various open source projects he learnt a few things the hard way. Martin enjoys figuring out complex problems and breaking them down, making them clear and accessible. He does this in his conference talks, on his blog at and in his book “Designing Data-Intensive Applications” (O’Reilly). You can find him as @martinkl on Twitter.