LakeLogic
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Build · Lakehouse Studio

Build governed data products faster. Accelerated by AI.

Provide business requirements, a source schema or sample data. LakeLogic proposes the product design, contracts, tests and implementation scaffold, grounded in your standards and reviewed by your team before delivery. Then explore and test the result on synthetic data.

Talk to usExplore the open source

AI-assisted design. Human-approved delivery.

Build · reviewable scaffoldExample
proposed from telematics_sample.csv18 columns · 5,000 rows read
contracts/vehicles/_system.yamlProposeddomain, ownership and environments inferred from the estate
contracts/vehicles/telematics_bronze.yamlProposedtypes, nullability and 11 quality rules from the sample
contracts/vehicles/telematics_silver.yamlProposeddedup on `vehicle_id, ts` · 2 PII fields tagged and masked
pipelines/vehicles_medallion.pyScaffoldedBronze → Silver → Gold on your engine
Synthetic test dataGeneratedshaped to the contract, no real rows leave your platform
Ready for team reviewNothing published or deployed

What you get

8 capabilities

  • Go from requirements to a review-ready design faster

    start from business context, a connected source, schema or sample file

  • Test on synthetic data shaped by your contracts

    generated rows with a share deliberately broken to exercise your rules. Shows how the design behaves, not production values

  • See what a change does before the pull request

    build a draft and compare it with the published contract: column changes, rule changes and the rows side by side

  • Explore and query the result

    browse each table and its quarantined rows, run read-only SQL, save queries for your team and download a runnable bundle

  • Apply your organisation’s standards

    check proposed outputs against contracts, policies and approved patterns

  • Deliver through your existing Git workflow

    open a reviewable pull request without bypassing team approval

  • Use your existing execution engines

    generate supported patterns for Polars, DuckDB and Spark

  • Bring your own transformation logic

    reference external Python, PySpark or SQL where generation is inappropriate

How it works

3 steps

01

Provide inputs

Describe the requirement, connect a source, paste a schema or provide a sample file.

02

Review the proposed design

LakeLogic uses AI assistance to propose domains, contracts, tests and implementation patterns. Your team edits and approves the design.

03

Test, compare and deliver

Build the tables on synthetic data, compare the draft with what is published, then open a pull request for review.

Design it in Lakehouse Studio. Approve what ships.

Turn business requirements and source data into review-ready contracts, test them on synthetic data, then deliver the approved change through your existing Git workflow.

Talk to usExplore on GitHub