Question 1

Can TextQL query Parquet files through MotherDuck?

Accepted Answer

Yes. MotherDuck inherits DuckDB's ability to directly query Parquet, CSV, and JSON files. TextQL can discover and query these files alongside your relational tables. Just ask your question referencing the data you need — Ana figures out whether it lives in a table or a file and generates the right query.

Question 2

How does TextQL work with MotherDuck's shared databases?

Accepted Answer

TextQL discovers all databases your MotherDuck account can access, including shared databases from other users or teams. Ana can query shared data and join it with your own databases in a single question — making collaborative analytics seamless.

Question 3

Is MotherDuck fast enough for interactive analytics?

Accepted Answer

Yes. DuckDB's vectorized execution engine and columnar storage deliver sub-second query performance on most analytical workloads. MotherDuck adds cloud scalability on top. Combined with TextQL's natural language interface, the experience is genuinely interactive — ask, answer, refine.

Question 4

Can I join MotherDuck data with my Snowflake warehouse?

Accepted Answer

Yes. TextQL supports multi-source queries across any connected data source. You can ask a question that combines MotherDuck data with Snowflake, BigQuery, Postgres, or any other integration. TextQL orchestrates the cross-source query and returns a unified result.

Question 5

What SQL dialect does TextQL generate for MotherDuck?

Accepted Answer

TextQL generates DuckDB SQL, which is PostgreSQL-compatible with extensions for analytical functions, list/struct types, and file-reading capabilities. Ana is trained on DuckDB-specific syntax and functions, ensuring generated queries are optimized for DuckDB's execution engine.