I help companies optimize data streaming systems.

Vancouver, Canada
Joined December 2009
The Advanced Apache Flink training is now available as a self-paced, on-demand course!
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One of my biggest learnings of the past 12 months as a consultant was ditching hourly pricing. I no longer have an hourly rate. You shouldn't either: independentengineer.co/p/hou…
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So far, when data streaming and AI are mentioned together, it typically describes the problem of maintaining a fresh, relevant context: e.g. use stream-processing to join/aggregate/enrich data streams, and pass them to an agent as context for inference. And honestly, I'm sold.
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But is there anything else? E.g. using Kafka for agent-to-agent communication? Streaming long agent sessions? Workflow orchestration?
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If you enjoy working with large-scale data processing systems and you want to stay relevant, you have to learn Apache DataFusion. Slowly but surely, it's replacing many other tools. The RAD stack (Rust, Arrow, DataFusion) is the future. One example: datadoghq.com/blog/engineeri…
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I attended the Data Streaming Summit last week. Just published a post with a few highlights from the event: streamingdata.tech/p/data-st…
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Watermarks can be a really scary concept when you start working with stateful streaming systems. And all popular frameworks don't offer anything better than "bounded out-of-orderness": pick a number of seconds and hope that all data arrives within that interval.
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But when you do control the producers (e.g. application events), the producers themselves can be the source of watermarks! The idea is not new, and it's covered in several famous papers like Google's MillWheel (see the screenshot).
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It can actually be translated to modern Kafka producers emitting watermark signals. Anyone interested in a POC?
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Earlier today at the Data Streaming Summit. Thanks Tun Shwe for the picture! #DSS26
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Does anyone use IVM systems like Materialize or Feldera in production? What's your experience? Feel free to DM.
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A very cool FLIP (Flink Improvement Proposal) has recently been accepted: Running Flink jobs in MiniCluster using the Kubernetes Operator. cwiki.apache.org/confluence/…
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The FLIP addresses the problem by running both components (JobManager and TaskManager) in the same JVM process on a single pod. It's a big step in making Flink simpler and more efficient to operate.
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I especially like that you can easily migrate from this setup to a "classic" deployment by simply using a savepoint. Start small, then scale out.
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It's fascinating how many projects now offer a native CDC (Change Data Capture) experience. A few years ago, your choice was to use Debezium or ... *checks notes* that's it. Postgres logical replication for CDC has become almost... boring?
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Is this because of LLMs? Or have more engineers become database replication experts? 🤔
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