Welcome to bricksentry

The safety check for data changes in the AI era.

Change-safety for Databricks pipelines and the AI agents that edit them: know what a change does to your data before it ships.

Try it in 30 seconds · Check every change on Databricks · Docs: bricksentry.org · LinkedIn

Built by Brahmareddy, Databricks Champion. Open source under Apache-2.0.

Why the name? Bricks plus entry. Your tables and pipelines are the bricks of the lakehouse, and every change to them is an entry. bricksentry checks each entry, reports what it does, and stands guard before it lands.

Three steps, each on more real data

1. Try it in 30 seconds, on your laptop

uvx --from "bricksentry[duckdb]" bricksentry demo

(Or pip install "bricksentry[duckdb]" and then bricksentry demo.) No Databricks account and nothing leaves your machine: it runs on a 20,000-row revenue table in local DuckDB. An AI agent previews a DELETE on a clone (production untouched), gets its pull request blocked for a join that duplicates 40 orders, fixes it, and is routed to the owners of a certified table downstream. Then a nightly pipeline commits a bad MERGE anyway: bricksentry finds it in the table history, skips the OPTIMIZE after it, blocks it for 25 duplicate keys, prints the RESTORE and applies it.

2. Check every change to your tables, on Databricks

databricks bundle init https://github.com/bricksentry/bricksentry --template-dir templates/job-check
cd bricksentry_check && databricks bundle deploy
databricks bundle run bricksentry_demo     # optional: see a block on a demo table in about 2 minutes

One command and a few prompts give you a serverless job that runs after every commit to the tables you pick: the same check as the demo's last step, on your data, with the RESTORE that undoes a bad change and an optional Slack alert. The optional demo job writes a known-bad MERGE to a demo table in your scratch schema and checks it, so your first run ends red with a block. Details below.

3. Stop bad changes before anything reads them

Put the check inside your pipeline job, right after the task that writes the table: a block fails that task and the tasks downstream don't run. Or run it on pull requests with the GitHub Action, and give AI agents the same checks through the MCP server.

Run it in your Databricks workspace

What did your last pipeline run do? (10 minutes, read-only)

Check-my-table notebook: in your workspace, go to Workspace → Import → URL and paste `https://raw.githubusercontent.com/bricksentry/bricksentry/main/examples

... View full README on GitHub