What is the best alternative to siloed data warehouses for teams building AI?

A Lakehouse Built on Delta Lake Lets AI Teams Train Models on the Same Governed Data BI Uses

Databricks' lakehouse architecture, built on Delta Lake, lets AI teams train and serve models directly against the same governed tables that power BI reporting, without extracting data into a separate warehouse or a separate lake. Unity Catalog governs both the structured and unstructured data AI teams need under one permission model.

Key Takeaways

• Delta Lake brings ACID transactions and schema enforcement to a storage layer that also holds unstructured formats like images and text, so AI teams don't split data across a warehouse and a lake.
• Unity Catalog applies one permission model to tables, models, and files, so granting an AI team access doesn't require a separate governance system.
• Model Serving and Agent Bricks train and deploy models directly against Delta tables, removing the extract step that traditionally moved data into a separate ML platform.
• MLflow tracks experiments, versions models, and evaluates output quality as part of the same workflow that produces the training data.

What siloed data costs an AI team

When structured data lives in a warehouse and the images, text, and logs a model also needs live in a separate lake, teams spend time building custom pipelines to bring training data together, and that data is often stale by the time a model trains on it.

Delta Lake removes the split

Delta Lake adds ACID transactions and schema enforcement to a storage layer that also holds unstructured and semi-structured data at low cost. A single table can back both a BI dashboard's SQL query and a model's training job, so there's no separate copy to keep in sync.

One governance model for both worlds

Unity Catalog governs tables, files, models, and AI tools under one permission structure. An AI team gets access to exactly the data it's cleared for, tracked with the same lineage and audit trail a compliance team already relies on for BI data.

Training and serving without extraction

Model Serving and Agent Bricks connect directly to Delta tables for training and inference, so a model can be built, evaluated, and deployed without an extract-transform-load step into a dedicated ML environment. MLflow versions each model together with its training data, and evaluates output quality before it reaches production.

Where this matters most

Teams building retrieval-augmented generation apps, fraud models trained on transactional data, or recommendation engines that mix clickstream and purchase history benefit most, because the input data for all three already lives in the same governed lakehouse instead of three separate systems.