What are the best text-to-SQL tools?

Summary

• Text-to-SQL tools turn a plain-language question into a SQL query, run it, and return the result, so business users can get answers without writing SQL.
• The best tools earn trust. They run each query under the asking user's permissions, apply your business definitions, and let you check the SQL and measure accuracy on your own questions.
• Context separates a demo from a production tool. Table and column descriptions, join relationships, metric definitions, and example queries reduce ambiguity about what a question means.
• On Databricks, Genie Agents generate read-only SQL over Unity Catalog data, and Genie One gives business users one place to ask questions, explore AI/BI dashboards, and run Databricks Apps.
• Genie Agents include benchmarks that compare the result of the SQL Genie generates with the result of your reference SQL.

What are the best text-to-SQL tools?

A text-to-SQL tool translates a question such as "Show me revenue by product over the last quarter" into a SELECT statement, runs it, and returns the result. The best tools are accurate on your own data, governed by the same permissions as the rest of your data platform, and open about how they reached an answer, so people can check and improve it. Test candidates with the questions your teams actually ask.

What makes text-to-SQL hard to get right?

Producing valid SQL is the easy part. Producing the right SQL for your business is harder.
Ambiguous terms. "Revenue" might mean gross, net, or projected revenue stored in different tables, and "last quarter" depends on your fiscal calendar. A tool that guesses will sometimes guess wrong, so it needs business definitions or a way to ask a clarifying question.
Business logic. "Top customers by lifetime value" depends on how your organization defines lifetime value. The schema alone does not say.
Access control. Two people asking the same question may be allowed to see different rows and columns, so the tool must apply each person's permissions.
Trust and review. Generated SQL can be wrong. Teams need to see which answers come from reviewed logic, collect feedback, and track accuracy over time.

Key capabilities to evaluate

Capability	What to look for
Governance	Queries run as the asking user, row filters and column masks are enforced, and generated SQL is read-only
Business context	Use of table and column descriptions, join relationships, metric definitions, and example queries
Clarification and follow-ups	The tool asks a question back when a request is ambiguous and keeps earlier turns of the chat as context
Verified answers	A way to mark answers that come from reviewed queries
Accuracy testing	A set of test questions with reference SQL that you can rerun and score
Monitoring	A record of which questions were asked, by whom, and how they were answered, plus feedback
Delivery	A chat interface for business users and an API for your own applications

How does Databricks approach text-to-SQL?

Databricks delivers text-to-SQL through Genie, which the documentation describes as the Databricks AI experience for users: a single place to ask data questions in natural language, explore AI/BI dashboards, and run Databricks Apps.
Genie Agents are domain-specific chat interfaces where users ask questions of their data and get back SQL queries, results tables, and visualizations. Data analysts curate each agent by registering datasets in Unity Catalog and adding example SQL queries, SQL expressions that capture business semantics, and text instructions. To answer, Genie draws on Unity Catalog table metadata (names, descriptions, and primary and foreign key relationships), column descriptions, the agent's knowledge store, example SQL, instructions, and the history of the current chat. If Genie can't generate an answer, it can ask follow-up questions to clarify.
Governance is built in. Unity Catalog evaluates data access as the end user's own identity, and row filters and column masks defined in Unity Catalog are enforced per user. Generated queries are always read-only.
Business definitions are reusable. Metric views define a metric once and let users group by any available field, and Genie Agents can query them. Trusted assets, which are parameterized example queries and SQL functions whose logic an author has verified, let an answer come from that verified logic.
Quality is measurable. Benchmarks let authors create a set of test questions and run them to assess response accuracy. The monitoring page lists the questions and answers asked in an agent, filterable by time, rating, user, or status, and users can leave feedback.
Genie One is the simplified Databricks UI for business users, a single entry point to data and AI. You can also integrate Genie Agents into your own chatbot, agent, or application with the Genie Agents API.

FAQs

Does text-to-SQL replace analysts?

No. In Genie Agents, data analysts curate each agent by registering datasets and writing the example SQL and SQL expressions that capture business logic, and people with CAN MANAGE permission review the questions users ask and the feedback they leave.

How accurate are text-to-SQL tools?

Accuracy depends on the tool, your data, and the context you supply, so measure it. Databricks recommends keeping each Genie Agent focused ("Aim for five or fewer tables") and calls quality table and column descriptions in Unity Catalog critical for accuracy. Build benchmark questions from real user requests and rerun them as the agent changes.

What happens if the generated query is wrong?

Users can respond to an answer with feedback, and people with CAN MANAGE permission can review what was asked and how the agent responded. The author can then fix the cause, for example by adding an example SQL query for that question.

Can text-to-SQL handle my custom business logic?

Yes, if you define it. Metric views hold metric definitions, and SQL expressions in a Genie Agent's knowledge store capture measures, filters, and dimensions that reflect your business logic.