duckdb is becoming a kind of data superglue between a lot of data ecosystems (GIS, observability, analytics, lakehouses, object storage, etc) that don't talk to each other typically, and it's worth checking out in 2026.

* https://github.com/duckdb/extension-template * https://duckdb.org/community_extensions/

That said… think duckdb/ducklake/quack could potentially be a future replacement for Mimir or Clickstack with way less operational complexity.

This makes Ducklake much more interesting for my use case, excited where this is going.

[0] https://github.com/smithclay/duckdb-otlp

* https://fly.io/blog/design-and-implementation/ * https://juicefs.com

I'm coming at it from more of the data infrastructure side (e.g. send all of your logs and metrics to a cheap Iceberg catalog in the cloud so you have a central place to query[1]) but also check out https://github.com/tobilg/ai-observer -- duckdb popping up everything to make this interesting and easy.

[1] https://github.com/smithclay/otlp2pipeline

For customers, it makes migrations much easier and less-risky between vendors.

For the vendors themselves, it means you can cheaply and reliably port features your competitors have that you don’t have.

Posted this yesterday as well, but seems like a really nice emerging pythonic way to call out to remote infrastructure (see: Modal[1]).

[1]: https://modal.com/docs/examples/hackernews_alerts#defining-t...

[1] https://modal.com/docs/examples/hackernews_alerts [2] https://docs.ray.io/en/latest/ray-overview/getting-started.h...

edit: forgot to mention StackQL [3], which is somewhat similar but takes a SQL-first vs python-first approach. The IaC space is about to get a lot more interesting.

[3] https://stackql.io/docs

The only other benchmark I've come across is https://sreben.ch/ ... certainly there must be others by now?

https://github.com/anthropics/claude-code/tree/main/plugins/...

Snowflake acquired Observe last week, AWS made it easy in December to put logs from Cloudwatch in their managed iceberg catalog, and Azure is doing a bunch of interesting stuff with Fabric.

The line between your data lake/analytics vendor and observability vendor is getting blurry.

Below are two examples using duckdb under the hood for similar purposes. Like the author, excited for this type of architecture making semi-advanced analytics more attainable if you’re not a data engineer.

- https://pandas-ai.com/ - https://marimo.io/

Think this opens up a lot of interesting possibilities like more powerful analytics notebooks like marimo (https://marimo.io/) … and that’s just one example of many.

All major vendors have a nice dashboard and sometimes alerts to understand usage (broken down by signal type or tags) ... but there's clearly a need for more advanced analysis which Tero seems to be going after.

Speaking of the elephant in room in observability: why does storing data on a vendor cost so much in the first place? With most new observability startups choosing to store store data in columar formats on cheap object storage, think this is also getting challenged in 2026. The combination of cheap storage with meaningful data could breathe some new life into the space.

Excited to see what Tero builds.

Lots of different technical solutions for how to do this, including the Claude and ChatGPT mobile apps nowadays. I use Tailscale. Choose what works best for you and enjoy.

Comments URL: https://news.ycombinator.com/item?id=46213753

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Forgot OTLP log bodies could be arrays/non-strings and was not a test case.

Yesterday, AWS made this exact sort of data architecture lot easier with new CloudWatch features: https://aws.amazon.com/blogs/aws/amazon-cloudwatch-introduce...

Comments URL: https://news.ycombinator.com/item?id=46152772

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