Introduction
In the current Gen AI buzz, most conversations focus on RAG for unstructured documents. But there’s another equally important challenge — making sense of structured data at scale.
This is where tools like Databricks Genie step in, enabling “text-to-SQL” for business users and analysts. It’s also the reason I wrote this article — to unpack how Databricks is re-imagining modern data warehousing for the AI era.
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Traditional data warehouses come with their baggage: complex infrastructure, slow performance at scale, and headaches with governance and compliance. Databricks changes that with SQL on the Lakehouse, powered by Unity Catalog and Delta Lake.
Here’s what it brings to the table:
- Unified data management under one governance framework.
- Easy transformations with Delta tables and Medallion architecture.
- AI-ready outputs for analytics, dashboards, and ML models.
The unified architecture in Databricks looks as follows:
The data from data sources is ingested, transformed, queried, visualized, and served to external apps. All of these transformations are powered by governance (provided by Unity Catalog) and deliver a strong price vs performance.
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To summarize, one architecture to ingest, transform, query, visualize, and serve data… with governance baked in.
Two main personas benefit from Databricks’ warehousing approach:
- Analysts → Building AI/BI dashboards.
- Business users → Asking natural language questions in Genie.