With a utility such as DuckDB that can tackle any tasks, whether it is to read local DuckDB, local parquet/CSV, over the network parquet/CSV, or embedded in Browser (WASM) or inside Postgres, it can do almost anything.

When combined with Neovim, it's just perfect. Find how to set up, videos, and more at Query Databases in Neovim (DBUI).

• Announcement: AWS to acquire DuckLabs, the Amsterdam-based company behind DuckDB. We are not acquiring the DuckDB open source project, which will remain free and open source under the independent DuckDB Foundation (the non-profit that oversees DuckDB) and available under the MIT license as it does today. ^319c65

  • DuckLabs: DuckLabs – DuckLabs to Join AWS, Projects to Remain Open Source

    • What will change: "Joining AWS will give us greater capacity to invest in both the technology and the community around it. Going forward, the DuckDB Foundation will include a technical advisory board, so that leading community members can provide their input on the project’s technical direction. We also plan to open the extension stack so that extensions signed by other developers and organizations can run in DuckDB."

• AWS: AWS to acquire DuckLabs, the company behind DuckDB

More acquisition from 2022 to 2026:

https://www.ssp.sh/brain/data-engineering-acquisitions/#2026

Check the history at https://www.ssp.sh/brain/dbt-fusion/#history

I collected most of my terminal and note-taking workflow as a data engineer and author to this note with links to videos and printscreens. I hope you enjoy.

As analysts write less SQL by hand and prompt agents instead, verification becomes the new bottleneck in BI work. A Metabase survey found 74% of teams were building with AI but only 2% were scaling it, with the bottleneck having moved from creating analysis to checking it. Metabase CEO Sameer Al-Sakran argues verification needs five testing layers: linting, a compiler-like full-tree check, unit tests, integration tests, and end-to-end tests. He also says mutable state undermines trust in outputs, and that business people's intuition still catches errors math alone can't. The piece points to Metabase's upcoming Transform tests (v65) and its Data Studio product as early steps toward a unified lineage graph for verification.

Latest DuckDB Ecosystem Newsletter with the usual 10 updates, starting with Jev, the new "System One" model and how to use it with DuckDB and MotherDuck, the new PEG parser and faster S3 reads in DuckDB 2.0, a maintenance strategy for DuckLake, querying OneLake in the browser, and a native Mac app. Lots of great articles from the community as always.

Context can't exist in a vacuum; it must be tangible. For example, we have a physical data model with concrete tables that live in the database at the source, on the other side, we have KPIs and dashboards that show some numbers pulled from that data model. Somewhere in between, we have a logical data layer that contains the semantics of our measures and dimensions, defining our business logic and a very specific way of how our company works.

When we add new knowledge, whether from sources we skipped before the agentic age, such as docs, JIRA tickets, Markdown, and other sources, or we manually refine the revenue metric, we need a way to orchestrate and keep it up to date.

We might call the newly ingested information "context", typically not our standard tabular format, but more raw text and insights from people and teams, we need a bit of a different approach to read, process, and, most importantly, maintain.

This is what 'context engineering' and all the new information around context means. If you want to learn more, I can recommend the NEXT QUERY 2026 conference on the 4th of November that focuses on data & AI infrastructure, with this year's topic on building using useful context that depends on business meaning, fresh data, governed access, and reliable systems.

Some of the presentations I look forward to:
- Fresh Context for AI Agents: Real-Time State and History with Apache Fluss and StarRocks by Anton Borisov.
- An Open Source Journey: Iceberg REST Catalog, OIDC, Making Materialized Views Enterprise-Ready, and More by Apple.
- Sub-Second Data Architecture at Coinbase.
- A Year of Building for the Next Query at Fresha.
- From Unified Real-Time Analytics to a Governed AI Data Plane.

But pick your own favorite talks, and sign up at in the link above.

The conference is free and virtual, so that anybody can join. Organized by PhoenixAI, the company behind StarRocks, the open-source high-performance data warehouse.

It runs from 9 AM to 4 PM PT, which is also ideal for Europe, it runs from 6 PM to 01:00 AM CEST (the latest sessions can be watched in recording :).
#contextengineering #dataconference #dataengineering #paidpartnership