u/sspaeti

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.

I've just updated my entry page to make the connection between the brain book and blogs more visually clear. The nodes are clickable and point to actual notes in my second brain (there's 1000s more). What do you think?

If you are curious, the second brain (built on Quartz) is fully open-source.

All my battlestations on the go.

On the image:

• Kinesis Advantage Keyboard 2
• Lenovo ThinkBook 14 G7 ARP
• MicroJournal Rev. 2 (small distraction-free typewriter, running Raspberry Pi Zero 2W)
• On Screen: Tmux, Omarchy Arch Linux and Neovim

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?