Question 1

Does TextQL work with Delta Lake and Unity Catalog?

Accepted Answer

Yes. TextQL connects directly to Databricks SQL Warehouses and fully supports Delta Lake tables managed through Unity Catalog. Ana reads catalog, schema, and table metadata — including descriptions, tags, and column-level comments — to understand your data model and generate accurate Spark SQL queries.

Question 2

Can TextQL query data across my medallion architecture layers?

Accepted Answer

Absolutely. TextQL understands multi-layer data architectures. When you ask a question, Ana intelligently selects the most appropriate table — preferring curated gold-layer tables when available, and falling back to silver or bronze when needed. You can also explicitly specify which layer to query in your question.

Question 3

How does TextQL connect to Databricks?

Accepted Answer

TextQL connects through Databricks SQL Warehouses using either a personal access token or service principal credentials. All queries execute inside your Databricks environment, and TextQL never copies or stores your data. Setup takes under two minutes — just provide your workspace URL and credentials.

Question 4

Can I query Databricks and another warehouse at the same time?

Accepted Answer

Yes — this is one of TextQL's most powerful features. Ana can run a single natural language query that joins data from Databricks with data in Snowflake, Postgres, or any other connected source. TextQL handles the cross-warehouse orchestration and returns a unified answer.

Question 5

What if my Databricks tables have thousands of columns?

Accepted Answer

TextQL handles wide tables efficiently. Ana uses schema metadata, column descriptions, and usage patterns to identify the most relevant columns for your question. It won't scan unnecessary columns, keeping queries fast and cost-effective even on tables with hundreds or thousands of fields.