How does type inference work?

The tool inspects keys across objects and picks scalar types, with overrides for ambiguous fields.

Are nested JSON fields supported?

Nested objects can be flattened or preserved depending on the dialect and options you select.

Can I try it in the browser?

Yes. The demo on this page covers typical payloads; the desktop app adds offline history and sidebar access.

JSON to CREATE TABLE

Generate CREATE TABLE statements from JSON payloads for Postgres, BigQuery, and Snowflake without pasting sample records into a web form. DataEngUtils infers column names and types from representative objects, handles nested structures with sensible flattening options, and outputs dialect-specific DDL you can paste into migrations or ad-hoc sandbox setup. Analytics engineers use it when product teams ship event samples, when APIs change fields mid-sprint, or when you need a quick staging table before writing a dbt source block — all without exposing customer JSON to a third-party server.

Try it free

InputOutput

How it works

1. Paste sample JSON
Use an array of objects or a single document that represents your production shape.
2. Pick a warehouse dialect
Choose Postgres, BigQuery, or Snowflake syntax for the CREATE TABLE output.
3. Copy DDL
Paste the generated statement into migrations, docs, or a scratch worksheet.

Why not use an online tool?

Online converters tools require uploading your pipeline inputs to a third-party server. For production work, that is a security and compliance risk. DataEngUtils runs locally — same convenience, none of the exposure.