rmoff's random ramblings

Interesting links - February 2026

by · Interesting Links at https://preview.rmoff.net/2026/02/27/interesting-links-february-2026/

Table of Contents
Interesting Links

Phew, what a month! February may be shorter but that’s not diminished the wealth of truly interesting posts I’ve found to share with you this month.

As this compendium grows in popularity, I’m keen to hear from you 🫵🏻 with your thoughts on:

  • What do you want more of?

  • Less of? (unless it’s AI; that stuff is staying whether we like it or not…)

  • Fewer links?

  • More commentary?

Use the comment section below, or feel free to email me directly.

As is usual (and my right as the author 😁) I’ll share my own articles first. It’s a good mix this month: some AI in practice, some AI contemplative thoughts, some real data engineering (that even r/dataengineering liked!):

I was also honoured to be a guest on Dan Beach’s podcast: The Evolution of Software, Streaming, and Data Engineering with Robin Moffatt, and interviewed for Cynthia Dunlop and Piotr Sarna’s blog: Robin Moffatt on Technical Blogging

D’ya like DAGs? (or data engineering in general?) 🔗

underCurrent: Data Engineering

Confluent are putting on a one-day conference for data engineers and architects in San Francisco next month (March!)

  • Speakers to include Joe Reis, Max Beauchemin, and Holden Karau

  • No vendors, no salesfolk

  • Completely free

  • March 26, in SF

  • Limited to 100 attendees; for full details and to register for free head here

dags

So…on with the interesting links!

  • 🔥 Not got time for all this? I’ve marked my top reads of the month :)

  • 📧 Want to receive this monthly round-up as an email? Subscribe to my Substack where I cross-post the same content

  • 🔗 Medium posts often skulk behind a gate, so I’ve hyperlinked to the Freedium version. You’ll see [Medium ↗] next to each link if you prefer the original.

Kafka and Event Streaming 🔗

Stream Processing 🔗

Analytics 🔗

Data Platforms & Architectures 🔗

Data Engineering and Pipelines 🔗

Data Modelling 🔗

CDC 🔗

  • 🔥 The Debezium project are inviting submissions to their Debezium 2026 Community Feedback Survey (open until April 5th).

  • 🔥 Yaroslav Tkachenko has been looking at the performance of three different CDC tools (Debezium, Flink CDC, and Supermetal) taking data from Postgres into Kafka, running a set of benchmarks covering both snapshots and live CDC. Which won? You’ll have to read the article (hint: Rust & Arrow apparently is rather quick!)

  • A couple of interesting posts on the Debezium blog this month, looking at Measuring Debezium Server Performance, and a new feature being introduced to provide Reusable Connections in Debezium.

  • A reddit thread discussing the pros and cons of using CDC and Kafka to synchronise two databases, vs dedicated replication software.

  • 🔥 Excellent deep-dive troubleshooting post from the team at Zepto, covering Debezium performance problems (and fixes made).

  • Chandrasekar Gnanasambandam and team at Guidewire show how they reduced Debezium snapshot duration by 70%.

Open Table Formats (OTF), Catalogs, Lakehouses etc. 🔗

RDBMS 🔗

General Data Stuff 🔗

AI 🔗

I warned you previously…this AI stuff is here to stay, and it’d be short-sighted to think otherwise. As I read and learn more about it, I’m going to share interesting links (the clue is in the blog post title) that I find—whilst trying to avoid the breathless hype and slop.

Big Picture & Culture 🔗

AI’s impact on Open-Source Projects 🔗

  • 🔥 Important article from RedMonk’s Kate Holterhoff looking at the impact of AI on open-source projects. By the very definition of OSS, they are open to external contributions—and the sheer scale at which AI agents can churn out code is causing problems: AI Slopageddon and the OSS Maintainers. Steef-Jan Wiggers covers a similar subject in his InfoQ article.

  • 🔥 Tomas Vondra argues that previously there was "proof of work" that ensured that well-intentioned contributors in effect demonstrated their willingness to put in the time proportional to that of the maintainers required to review contributions: The AI Inversion

  • Ultimately, code contributions are the responsibility of the human interacting with the agent. Used well, coding agents are fantastic productivity boosters and enable people to create code they could never have done before. Used irresponsibly, it’s little better than monkeys throwing crap around in a zoo. Mitchell Hashimoto has conceived Vouch as a way for projects to deal with the influx of interactions, ultimately forming a web of trust for contributors.

AI in Software Engineering 🔗

AI in Practice 🔗

And finally… 🔗

Nothing to do with data, but stuff that I’ve found interesting or has made me smile.