Natural Language to SQL Generator

Create SQL queries from natural language questions.

SQL Mocker turns business questions into SQL using your reviewed schema, relationships, and generated dummy data, so you can inspect the query before running it in your real database.

Start building SQL View text to SQL page

Ask data questions

Describe the result you want, such as totals, rankings, comparisons, filters, or segments.

Use your schema

Generated SQL is based on reviewed tables, columns, data types, keys, and relationships.

Check the result shape

Use generated dummy data to review output columns, joins, filters, and aggregations before you copy SQL.

Natural language to SQL with review built in

Natural language to SQL works best when the assistant understands your database structure. SQL Mocker gives you a safe place to review the schema first, then generate and test SQL against mock results.

Create a schema replica from uploaded, pasted, built, or extracted metadata.
Confirm primary keys, foreign keys, and relationship paths.
Ask a natural language question about the dataset or metric you need.
Review the generated SQL and dummy-data output before using it elsewhere.

Example

From question to SQL

A natural language question is converted into SQL using reviewed schema context.

Which customer segments had more than 25 orders in 2026, and what was their average order value?

Generated SQL based on reviewed tables, columns, and relationships.

SELECT
  c.customer_segment,
  COUNT(o.order_id) AS order_count,
  AVG(o.total_amount) AS average_order_value
FROM customers AS c
INNER JOIN orders AS o
  ON o.customer_id = c.customer_id
WHERE o.order_date >= DATEFROMPARTS(2026, 1, 1)
  AND o.order_date < DATEFROMPARTS(2027, 1, 1)
GROUP BY c.customer_segment
HAVING COUNT(o.order_id) > 25
ORDER BY average_order_value DESC;

AI-generated SQL may contain errors. Review and test before use.

Safer SQL generation

Ask questions without giving AI direct access to your real database.

SQL Mocker works from schema metadata and generated dummy data. It does not request database credentials, connection strings, or production rows.

That makes it useful when you want help creating SQL, but still want to review the query yourself before running anything in a real environment.

Natural language to SQL generator FAQ

What is a natural language to SQL generator?

A natural language to SQL generator turns a written question into a SQL query. SQL Mocker adds schema and relationship context so the query is easier to review before use.

Do I need to connect SQL Mocker to my database?

No. SQL Mocker uses schema metadata and generated dummy data. You do not need to provide database credentials or connect to production data.

Can I use it with existing database schemas?

Yes. You can upload, build, paste, or extract schema metadata, then generate SQL based on the reviewed tables, columns, and relationships.

Should I still review the generated SQL?

Yes. AI-generated SQL can contain mistakes, so you should review, test, and validate it before using it in a real database, report, or workflow.