Stream Processing In Real Time And At Scale In Pure Python With Bytewax
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Summary

Analysis of streaming data in real time has long been the domain of big data frameworks, predominantly written in Java. In order to take advantage of those capabilities from Python requires using client libraries that suffer from impedance mis-matches that make the work harder than necessary. Bytewax is a new open source platform for writing stream processing applications in pure Python that don’t have to be translated into foreign idioms. In this episode Bytewax founder Zander Matheson explains how the system works and how to get started with it today.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Zander Matheson about Bytewax, an open source Python framework for building highly scalable dataflows to process ANY data stream.

Interview

• Introductions

• How did you get introduced to Python?

• Can you describe what Bytewax is and the story behind it?

• Who are the target users for Bytewax?

• What is the problem that you are trying to solve with Bytewax?

• What are the alternative systems/architectures that you might replace with Bytewax?

• Can you describe how Bytewax is implemented?

• What are the benefits of Timely Dataflow as a core building block for a system like Bytewax?

• How have the design and goals of the project changed/evolved since you first started working on it?

• What are the axes available for scaling Bytewax execution?

• How have you approached the design of the Bytewax API to make it accessible to a broader audience?

• Can you describe what is involved in building a project with Bytewax?

• What are some of the stream processing concepts that engineers are likely to run up against as they are experimenting and designing their code?

• What is your motivation for providing the core technology of your business as an open source engine?

• How are you approaching the balance of project governance and sustainability with opportunities for commercialization?

• What are the most interesting, innovative, or unexpected ways that you have seen Bytewax used?

• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bytewax?

• When is Bytewax the wrong choice?

• What do you have planned for the future of Bytewax?

Keep In Touch

• Slack)

• Twitter)

• LinkedIn)

Picks

• Tobias

• Alta Racks)

• Zander

• Atherton Bikes)

Links

• Bytewax)

• GitHub)

• Flink)

• Data Engineering Podcast Episode)

• Spark Streaming)

• Kafka Connect)

• Faust)

• Podcast Episode)

• Ray)

• Podcast Episode)

• Dask)

• Data Engineering Podcast Episode)

• Timely Dataflow)

• PyO3)

• Materialize)

• Data Engineering Podcast Episode)

• HyperLogLog)

• Python River Library)

• Shannon Entropy Calculation)

• The blog post using incremental shannon entropy)

• NATS)

• waxctl)

• Prometheus)

• Grafana)

• Streamz)

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra) / CC BY-SA)