Coginiti + Databricks
Semantic Intelligence for the Databricks Lakehouse
Databricks gives you one platform for data, analytics, and AI. Coginiti gives the people who work on it a governed way to discover, transform, test, and define business meaning on top of Delta Lake and Unity Catalog, then serve that meaning to every tool and agent that needs it.
Better Together
Databricks runs the data. Coginiti runs the meaning.
Databricks unified the lakehouse: open table formats, elastic SQL warehouses, and a single governance plane in Unity Catalog. It is where an increasing share of enterprise data and AI workloads now run.
What the lakehouse does not do on its own is capture how your organization thinks about that data. Which query is the trusted definition of revenue? Which tests must pass before a table is promoted? Who reviewed the transformation, and why did they make the choices they made?
Coginiti sits alongside Databricks as the place where that knowledge lives. Practitioners develop and test SQL against Databricks in the Data Workbench, promote it into versioned CoginitiScript packages, and publish governed definitions to a semantic layer that BI tools, applications, and AI agents all share. Databricks runs the compute. Coginiti operationalizes the meaning.
Why Coginiti and Databricks Work Together
Delta Lake and Iceberg
Open formats, governed logic.
Coginiti supports Databricks with Delta Lake and with Apache Iceberg tables. Your transformation and semantic definitions stay independent of storage format, so a table migration never means a logic rewrite.
Unity Catalog
Governance for data. Governance for meaning.
Unity Catalog governs access to tables and files. Coginiti governs the analytic logic and metric definitions built on them, with review workflows, version history, and an audit trail for every promoted asset.
Multi-Platform Estates
Databricks rarely stands alone.
Most Databricks customers still run Oracle, Db2, SQL Server, or a second warehouse. Coginiti connects to 21+ platforms, so one semantic layer can span the lakehouse and everything around it.
Everything Coginiti Does on Databricks
From the first exploratory query to the governed answer an executive reads, one platform covers the full lifecycle.
Data Discovery
Find it. Profile it. Trust it.
Browse Unity Catalog catalogs, schemas, and tables from the Coginiti Database Explorer, profile columns in Delta tables with a click, and search the Analytics Catalog for the queries and models your team has already built on Databricks before you write a new one.
Explore Data WorkbenchData Transformation
Modular, governed transformation in the language of SQL.
Author modular, parameterized transformations in CoginitiScript that compile to Databricks SQL. Reuse blocks across notebooks-worth of logic, version every change, and run pipelines against SQL warehouses without leaving the workbench.
Explore CoginitiScriptData Quality Testing
Encode assumptions. Validate everything.
Write schema, uniqueness, completeness, integrity, and volume tests as CoginitiScript test blocks that run on Databricks. A test passes when its query returns zero rows; a failing critical test can stop a pipeline before bad data lands in a gold table.
Explore CoginitiScript testingSemantic Layer
Define once. Trust everywhere.
Define entities, dimensions, measures, and relationships over Delta and Iceberg tables once. Serve them through JDBC and ODBC to BI tools and through semantic SQL to AI agents, so every consumer of the lakehouse computes the same metric the same way.
Explore Semantic LayerCoginiti Forge
An AI co-developer for the people who build the foundation.
Forge is the co-development agent for Databricks practitioners. It helps define the semantic graph over Unity Catalog, drafts CoginitiScript transformations and tests, and encodes business context into the assets it touches, so knowledge survives beyond the engineer who wrote it.
Explore AI AgentsCoginiti Guide
Governed answers for stakeholders who need BI, not SQL.
Guide answers business questions using the governed definitions your team published, not raw Delta tables. Stakeholders get consistent numbers, tables, and charts from Databricks data without needing SQL or a seat in a notebook.
Explore AI AgentsBuilt for Databricks
Connectivity
- •Native Databricks connectivity from the Coginiti Data Workbench
- •Authenticate with personal access tokens, OAuth 2.0, or Microsoft Entra ID for Azure Databricks
- •Databricks with Delta Lake and Databricks with Apache Iceberg both supported
- •Semantic layer served over JDBC and ODBC to Power BI, Tableau, and other BI tools
Where It Fits
- •Teams standardizing metrics across a lakehouse and legacy relational systems
- •Migrations from Hive, Spark SQL, or a warehouse onto Databricks that must keep business logic intact
- •Organizations grounding AI agents in Databricks data through governed definitions instead of raw tables
- •Regulated and classified environments where Databricks and Coginiti both deploy inside the customer boundary
See Coginiti on Databricks
Bring your own schema. We’ll walk through discovery, transformation, testing, and the semantic layer on your Databricks environment.
Don't take our word for it.
Ask your favorite AI assistant to weigh in on Coginiti.