Governed data product platform

Build data products teams can trust.
Stop bad data before it spreads.

LakeLogic enforces quality, governance and service levels from design to operation—blocking unsafe changes, containing bad data and helping teams resolve problems faster with Zeus AI.

Explore the platformTry in Google Colab
Illustrative scenario: 360 incidents a year (30 a month), six engineer-hours each, at a blended cost of €100 per person-hour. Actual costs vary.
Apache 2.0 open sourceRuns in your environmentDatabricksTechnology PartnerMicrosoftISV PartnerNVIDIAInception Program

Works with the stack you already run

  • Polars
  • DuckDB
  • Apache Spark
  • Databricks
  • Microsoft Fabric
  • Snowflake
  • BigQuery
  • Amazon S3
  • Azure ADLS Gen2
  • GitHub
  • GitLab
  • Azure DevOps
  • Bitbucket
  • Jira
  • Slack
  • Microsoft Teams
  • Power BI
  • Tableau

One platform for the governed data-product lifecycle

Understand what exists, build what is needed, enforce your standards and resolve failures before they spread.

Know what exists and what depends on it.

Bring together available schemas, ownership and lineage to understand what exists, what depends on it and where evidence is missing.

  1. Assets and schemas discovered.
  2. Ownership and classification assessed.
  3. Dependencies declared, imported or inferred.
  4. Missing evidence clearly identified.

Outcome

Dependencies are named rather than discovered afterwards, and impact analysis reads the lineage graph instead of a spreadsheet.

Understand your estate
marketing / google_analytics / silver / sessionsExample
Data productsmarketing domain
Data productOwnerClassificationDownstream
marketing.sessionsGrowth AnalyticsPII · restricted6 consumers
finance.revenue_dailyFinance OperationsConfidential11 consumers
crm.accountsSales SystemsPII · restricted4 consumers
iot.device_eventsPlatform EngineeringInternal2 consumers
Lineage resolved across 6 systems128 products

Start from a reference blueprint.

Delivery targets

Microsoft Fabric

Generate contracts, notebooks and pipeline definitions for a Fabric workspace.

Explore Microsoft FabricView the reference implementation

Databricks

Generate contract-driven lakehouse projects and execute supported workloads with Spark.

Explore DatabricksView the reference implementation

Snowflake

Govern Snowflake data products and generate supported SQL-native delivery assets.

Explore SnowflakeView the reference implementation

LakeLogic Core executes supported contracts with Polars, DuckDB and Apache Spark. Compare engine support

Start open. Add coordination when you need it.

LakeLogic Core

Run governed contracts in your pipelines.

  • Apache 2.0
  • OLC execution
  • Polars, DuckDB and Spark
  • CI checks
  • Quality rules and quarantine
  • Local runtime evidence
Start on GitHub

LakeLogic Platform

Manage the complete data-product lifecycle.

  • Estate understanding
  • Lakehouse design
  • Governance and approval
  • Deployment coordination
  • Operational evidence
  • Zeus investigations and remediation
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Your data stays in your environment.

Metadata, not dataThe LakeLogic Platform receives operational evidence, not your business rows.
You own the contractsContracts remain version-controlled in your repository.
Human approvalMaterial changes pass through review and your delivery workflow.
Open executionLakeLogic Core runs inside infrastructure you control.

Ready to build trusted data products?

See LakeLogic in action.

Book a demo
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