SQL has powered critical business systems for decades. 30 years later, it’s still powering what’s next. Explore the Azure SQL Database Foundations series to modernize workloads, scale for the future, and build intelligent apps faster:

Add -com.apple.CoreData.SQLDebug 1 as a launch argument and you can watch exactly what SwiftData is doing under the hood. Tip: don't delete it when you're done, just uncheck the box so it's ready when you need it again 🔍

An agent-first SQL helper needs an agent-first setup. "npx skills add realZachi/pg-jev" - tell your AI assistant to set it up and you're done And if you like reading docs, those are now live as well!

Modernizing recurring SQL ETL doesn’t require rewriting the workflows you already rely on. Declarative ETL is coming directly into Lakehouse, so SQL practitioners can keep working in familiar SQL workflows while using declarative patterns for common recurring tasks: ↳ APPEND for incremental ingestion ↳ AUTO CDC for change data capture ↳ REPLACE WHERE for targeted batch refreshes Databricks handles the incremental processing, update logic, scheduling, and orchestration needed to run them reliably.

i self-host a sqlite-backed MCP server on a $20/month VPS. last month the disk filled up, WAL checkpointing failed silently, and i spent two hours at midnight restoring from a backup i almost didn't have. the server costs $20. the oncall rotation i volunteered for costs sleep.

Creator of Sqlite on pull requests: "You say, oh, it's free. No. It's not free. What you're doing is asking me ... to maintain it for you, to to document it for you, to test it for you, to maintain it for you for the next 25 years. That's not free." Yep. Wise words from a wiser man than me. I've told people for the past decade and I have recent posts on here saying the same: the merge button is the easy part. Its the decade+ (Richard says 25 years) that follows where you've accepted the transfer of maintenance thats hard.

DuckLake: The Lakehouse That's Just SQL & Parquet [Talk Python to Me, Ep. 562] How many files does your query read before it reads any data? On some data lakes, you go through JSON and metadata files first, just to learn which Parquet files matter. #DuckLake# asks one SQL question instead. The metadata lives in a real database. The data stays in plain Parquet. That's the entire format. With Quack as the catalog, DuckLake handles 200 transactions a second under heavy contention. No other open table format comes close. In this episode of @TalkPython, host @mkennedy talks with @holanda_pe, lead developer for DuckLake at DuckLabs, and Guillermo Sanchez Dionis, who works on both DuckLake and the new Quack protocol – together they discuss all the details of DuckLake, Quack, and more. Watch the episode here or listen wherever you get your podcasts: