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What you can do with Open Lakehouse Compute Fabric

Open Lakehouse Compute Fabric (OLCF) is an Apache 2.0 Python platform with a console, REST API, CLI, SDK and MCP endpoint for lakehouse work, plus a Spark compute fabric you run on your own machines and an extension for Microsoft Fabric notebooks.

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jddevtime/open-lakehouse-compute-fabric on GitHub

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What do you want to do?

5 use cases

  • Run a full lakehouse platform on one machine

    Start the console, SQL, notebooks and sample Retail lakehouse on a laptop with Python only.

    View prompt
    Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Install it from the README Install section, run `olcf quickstart`, and report the console address and whether the sample Retail lakehouse loads. Verify by running one SQL query against the sample lakehouse and returning the row count.

  • Find what depends on a table and who owns it

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

    View prompt
    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.

  • Let Claude Code browse the catalog and run read-only SQL

    Connect Claude Code to the OLCF MCP endpoint to explore tables, follow lineage and query safely.

    View prompt
    Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric) through its MCP endpoint. List the catalog assets in the sample Retail lakehouse, then run one read-only SQL count on <table name> and return the result. Verify that the query was rejected if you ask for any write statement.

  • Run a Fabric notebook on your own Spark hardware

    Send a Fabric notebook to your own Spark workers with olcf.run_remote() and read OneLake directly.

    View prompt
    Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric) and its Fabric integration guide. Attach the olcf_fabric wheel to a Fabric Environment, call olcf.run_remote() on <notebook name>, and report the returned status and exit value. Verify that the target OneLake table has the expected row count after the run.

  • Build a Spark cluster from your own machines

    Enroll several machines as workers and run Spark jobs on the combined Standalone cluster.

    View prompt
    Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Install the Spark runtime with `olcf runtime install --with-jdk`, enroll <number of machines> workers, and report the cluster members. Verify that a test job runs and that the worker list shows each machine as enrolled.

How to use Open Lakehouse Compute Fabric

  1. Check you have what it needs: 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.
  2. Open jddevtime/open-lakehouse-compute-fabric on GitHub and read the README and license.
  3. Install OLCF using the Install section of the README.
  4. Run olcf quickstart.
  5. Open the console the quickstart reports and open the sample Retail lakehouse.
  6. Run a query in the SQL editor against the sample lakehouse and check that rows come back.

These steps come from our top use case, Run a full lakehouse platform on one machine, which also has a copy-paste agent prompt.

Open Lakehouse Compute Fabric alternatives · Best AI repos for Data and analytics

About this repo

Requirements

  • 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
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Questions about Open Lakehouse Compute Fabric

What is Open Lakehouse Compute Fabric?

Open Lakehouse Compute Fabric (OLCF) is an Apache 2.0 Python platform with a console, REST API, CLI, SDK and MCP endpoint for lakehouse work, plus a Spark compute fabric you run on your own machines and an extension for Microsoft Fabric notebooks. It is an open-source project on GitHub: https://github.com/jddevtime/open-lakehouse-compute-fabric.

Who is Open Lakehouse Compute Fabric for?

Data engineers, DBAs and platform owners who want a lakehouse and Spark compute they control, with or without a Microsoft Fabric tenant.

What can I do with Open Lakehouse Compute Fabric?

5 use cases so far, for example: Run a full lakehouse platform on one machine; Find what depends on a table and who owns it; Let Claude Code browse the catalog and run read-only SQL. Each has steps and a copy-paste agent prompt.

What do I need to run Open Lakehouse Compute Fabric?

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.

What are alternatives to Open Lakehouse Compute Fabric?

Other hot AI repos in Data and analytics: Kronos, DBT Charts, Brandpose, Idx, Zepp Bridge.

Is Open Lakehouse Compute Fabric safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.