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Apache Kafka® Anti-Patterns and How To Avoid Them
USS Enterprise NCC-1701 Warp Drive (Source: Bryan Alexander, CC BY 2.0, via Wikimedia Commons) As every “Trekky” (a fan of Star Trek) knows, in the Star Trek universe spaceships can travel at speeds faster than light using warp engines fuelled by antimatter. It turns out that antimatter is real enough in our universe, but very...
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November 08, 2023 -
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Machine Learning Over Streaming Kafka Data—Part 6: Incremental TensorFlow Training With Kafka Data and Concept Drift
In the “Machine Learning over Streaming Kafka Data” blog series we’ve been learning all about Kafka Machine Learning—incrementally! In the previous part, we connected TensorFlow to Kafka and explored how incremental learning works in practice with moving data. In this part, we introduce concept drift, try and reduce noise, and remove time! 1. Concept Drift ...
Learn MorePaul BrebnerOctober 17, 2023 -
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Machine Learning Over Streaming Kafka® Data—Part 5: Incremental TensorFlow Training With Kafka Data
In the “Machine Learning over Streaming Kafka Data” blog series we’ve been learning all about Kafka Machine Learning – incrementally! In the previous part, we explored incremental training with TensorFlow, but without the complication of using Kafka. In this part, we now connect TensorFlow to Kafka and explore how incremental learning works in practice with...
Learn MorePaul BrebnerSeptember 28, 2023 -
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Machine Learning Over Streaming Kafka Data—Part 4: Introduction to Incremental Training With TensorFlow
Incremental clicker games can be addictive. The kinds involving performing simple actions repeatedly to gain higher levels. (Source: Shutterstock) One of the goals of incremental learning is to train a model continuously from streaming data. Incremental learning from streaming data means you don’t need all the data in memory at once, and the model is as up-to-date...
Learn MorePaul BrebnerSeptember 12, 2023 -
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Q&A with FerretDB
A Ferret in the Wild (Source: Shutterstock) Recently I managed to track down some of the key people behind FerretDB, and they kindly offered to answer some questions I had about FerretDB, the software and the project. Below is my Q&A I conducted with Alexander Fashakin (Technical Writer, FerretDB), Peter Farkas (CEO, FerretDB) and Marcin Gwóźdź (Director...
Learn MorePaul BrebnerAugust 31, 2023 -
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How to Use MongoDB® Clients and FerretDB® With Instaclustr for PostgreSQL®
1. What is FerretDB®? When I first heard about FerretDB, my initial thought was what on earth is a ferret?! From my childhood I vaguely recalled that ferrets, weasels, and stoats were the “baddies” from “The Wind in the Willows”, but that was about it (see endnote [1]). Departing the animal kingdom (for the time...
Learn MorePaul BrebnerAugust 30, 2023 -
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Machine Learning Over Streaming Kafka® Data—Part 3: Introduction to Batch Training and TensorFlow Results
In Part 2 of this series, we introduced the steps needed for batch training in TensorFlow with some example Python code. In this next part we’ll have a look at some performance metrics and explore the results. 1. Performance Metrics Sometimes accuracy is all that matters! (Source: Shutterstock) It took me a reasonable amount of...
Learn MorePaul BrebnerAugust 23, 2023 -
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An Introduction to Apache Kafka® Metrics for Developers
The Flying Scotsman was the first steam locomotive to break the 100 miles per hour speed record (161 km/h way back in 1934) (Source: Shutterstock) The Flying Scotsman was a 1900’s (in service 1923-1963) steam locomotive built for speed and scale—the steam era equivalent of Big Data cloud technologies today. It was big (100 tons, 21m long,...
Learn MorePaul BrebnerAugust 02, 2023 -
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Machine Learning Over Streaming Kafka® Data—Part 2: Introduction to Batch Training and TensorFlow
As I mentioned in Part 1 of this series, we are looking at Machine Learning (ML) over streaming Apache Kafka® data. But rather than just jumping in—and immediately going over a fast-flowing waterfall (in a barrel, which people have actually attempted!)—I first need to get a good understanding of TensorFlow with some “still” (static and unchanging) data and batch learning to start with. This will be easier and repeatable before we encounter Kafka and streaming and changing data.
Learn MorePaul BrebnerJuly 26, 2023