DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to find what depends on a table and who owns it with Open Lakehouse Compute Fabric

Use OLCF Intelligence to follow table lineage and see the confidence and gaps for each edge.

Who it is forA data steward who must answer what depends on a table before changing or retiring it.

The problemBefore changing a table, a steward needs to know which notebooks, pipelines and reports read it, and whether those links are proven or only guessed.

The outcomeA lineage view of upstream and downstream assets, with each edge marked verified, derived, inferred or unknown, and a list of gaps with reasons.

What you need first

  • Python and a Linux machine (the README says it runs end to end on Linux)
  • Installation of the OLCF package as described in the README Install section
  • For the multi-machine fabric: a Spark runtime installed with the JDK, and machines that can reach the control plane
  • For the Fabric extension: a Fabric tenant you control, the olcf_fabric wheel in a Fabric Environment, and Entra and network setup

Steps

  1. Complete the quickstart so the platform has a catalog to scan.
  2. Open the Intelligence section of the console, or run olcf intelligence from the CLI.
  3. Search for the table and open its lineage view.
  4. Review each edge's confidence level and the listed gaps before making a change.

Prompt for your agent

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Run `olcf intelligence` against the local installation, and for <table name> list its upstream and downstream assets with the confidence level of each edge. Verify that every edge has a confidence value and that gaps are listed with reasons, not guessed.

Caveat. Lineage is only as complete as the scanned sources, and edges inferred by name are not proof of a real dependency.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page