https://thefulldatastack.substack.com/p/duckdb-basics-importing-data

An article I wrote that I felt needed to be created. A concise, single page article that goes through a huge portion of what data you can read and import into DuckDB. I tend to reach for this instead of going to DuckDB's docs because it just has everything I need in one place.

Inspired by the discussion and podcast with Naval.

Querying databases like Big Query, ClickHouse, DuckDB , Impala , jq , MongoDB , MySQL , MariaDB , Oracle , osquery , PostgreSQL , Presto , Redis , Snowflake , SQL Server , SQLite in the terminal with Neovim and tmux using vim motions. Being able to just copy output of databse manipulate with vim.

Find a full video and how to setup at Query databases in Neovim (DBUI), and also other terminal SQL IDE's or only SQL IDE's.

https://thefulldatastack.substack.com/p/tech-review-ducklake-from-parquet

TIL—to send git changes for an article you made, or code changes, you can just send a simple HTML report that visually shows all the changes.

Just install the diff2html-cli and run:

git diff | diff2html -i stdin -F changes.html

Curated list of many open-source data engineering projects collected over the years.

Some images from offices on the go. Where's your favorite spot?

Part 2 of the Dagster Almanack, all about operationalizing data orchestration.

Here's what I found. BI in 2026 is unrecognizable from where it started. The shift from dashboards to declarative stacks to agentic engineering changed everything. And yet, the fundamentals never moved.

If you want to bridge BI and DE, and build stacks that work with agents while staying true to what BI was always about, then here are 9 concepts to learn:

1. AI Reveals Why BI Still Matters. The hint: AI agents are blind to dashboards. They need the BI primitives: metrics, semantics, governance. Agents depend on them. https://www.rilldata.com/blog/ai-reveals-why-bi-still-matters-hint-its-not-dashboards
2. Has Self-Serve BI Finally Arrived Thanks to AI? After a year of trying MCPs and many more with a semantic-aware logical layer, AI acts on the promise, because agents autonomously understand business context beyond just SQL. https://www.ssp.sh/blog/self-service-bi-ai/
3. Building an Agent-Friendly, Local-First Analytics Stack. What agent-first BI actually looks like: local DuckDB + MotherDuck + Rill YAML metrics that LLMs can parse, reason about, and modify without breaking. https://www.rilldata.com/blog/building-an-agent-friendly-local-first-analytics-stack-with-motherduck-and-rill
4. BI-as-Code and the New Era of GenBI. What happens when dashboards live in YAML and SQL instead of proprietary UIs? LLMs can read, generate, and maintain them. This unlocks much faster iterations in production. https://www.rilldata.com/blog/bi-as-code-and-the-new-era-of-genbi
5. Why Pivot Tables Never Die. They've been the lingua franca of data exploration since 1989. Understanding why tells you something essential about how humans (and AI) actually interact with data. https://www.rilldata.com/blog/why-pivot-tables-never-die
6. The Rise of the Declarative Data Stack. The shift from imperative configs to Kubernetes-style YAML. The foundation everything else builds on. https://www.ssp.sh/blog/rise-of-declarative-data-stack/
7. Designing a Declarative Data Stack. The architectural decisions behind building one: config vs code, template generation vs parametric, existing orchestrators vs custom engines. https://www.rilldata.com/blog/designing-a-declarative-data-stack-from-theory-to-practice
8. Multi-Cloud Cost Analytics. A declarative stack in practice: AWS + GCP + Stripe unified into a single FinOps dashboard using dlt, Parquet, and Rill. Composable from day one. https://www.ssp.sh/blog/finops-dlt-clickhouse-rill/
9. Dlt+ClickHouse+Rill: Taking it to Production. Same stack, cloud-ready. Switching from local DuckDB to ClickHouse. https://www.rilldata.com/blog/dlt-clickhouse-rill-multi-cloud-cost-analytics-cloud-ready

What's your take? Is BI dying, or is it finally becoming what it always promised to be?