LakeLogic
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Solutions · On Databricks

Deliver a decentralised data platform on Databricks.

Give domain teams ownership of their data products without rebuilding ingestion, transformation and quality controls for every source. LakeLogic brings one contract-driven approach, from design and testing to delivery and monitoring.

DatabricksTechnology Partner
Unity Catalog · RideFlow workspaceDatabricks
Databricks Catalog Explorer in the RideFlow workspace: one Unity Catalog catalog per domain (business, marketing, marketplace and more), each with bronze, files and gold schemas, and the landing and lakelogic_logs volumes.

What you get

6 capabilities

  • LakeLogic Studio designs it

    domains, contracts and pipelines; test changes and generate a reviewable Databricks project

  • LakeLogic Core runs it

    executes the contracts on Databricks with PySpark: ingest, transform, validate and materialise

  • The LakeLogic Platform watches it

    governance checks on every pull request; reported quality and service levels over time

  • A catalog per domain

    each domain publishes into its own Unity Catalog catalog; products that span domains live in a shared one

  • Bad rows quarantined, not lost

    rows that break a rule are held with the reason, before they reach a Gold table

  • Already on Databricks? Govern it in place

    metadata only: table names, schemas, lineage and row counts, never row values

How it works

3 steps

01

Design

Model domains, contracts and pipelines in LakeLogic Studio, and test them on generated data.

02

Deliver

Studio opens a pull request with the Databricks bundle. Governance checks review it before it merges.

03

Run and monitor

Core runs the contracts in your workspace. The Platform tracks quality and service levels, and Zeus diagnoses failures.

Explore a working data mesh.

Synthetic reference project

Different sources shouldn't mean a different engineering approach every time. RideFlow shows LakeLogic standardising ingestion, transformation, validation and materialisation through data contracts.

A realistic mix of CSV, JSON, PDFs, images and Word documents runs through batch and streaming pipelines into domain-owned data products across seven domains. It deploys with one Databricks bundle; you need permission to create catalogs.

RideFlow on Databricks: YAML contracts and synthetic test data feed LakeLogic Core, which builds domain products in one Unity Catalog catalog per domain, shared multi-domain gold for consumers, quarantine for rejected rows and run metadata in the lakehouse, with optional telemetry to the LakeLogic Platform.

Deliver your Databricks platform with contracts.

See LakeLogic design, deliver and monitor a decentralised Databricks platform: domains, contracts, governance checks and Zeus, with your data staying in your workspace.