PostgreSQL data generator: realistic rows from your schema

A PostgreSQL data generator fills a Postgres database with rows you can test against — for local development, staging, CI, or load testing. The category splits in two: generators where you describe every column by hand, and schema-driven generators that read your tables and foreign keys and produce rows that fit. This guide covers what a data generator does, where the hand-built ones break, and how to generate referentially intact Postgres data in one command.

What is a PostgreSQL data generator?

A PostgreSQL data generator creates rows for a Postgres database. It comes in two kinds, and the difference decides how much work lands on you:

A field-by-field generator asks you to describe the data: pick a column, pick a type, map the relationships yourself. A schema-driven generator asks the schema — it introspects tables, foreign keys, types, and constraints, then generates rows that fit. The first is fast for a flat CSV. The second is the only one that scales to a relational schema.

"Dummy data" vs "fake data" vs synthetic data

Three phrases circle the same job, but they point at different tools. "Dummy data generator" and "fake data generator" are the most common ways people search; "synthetic data" is what the schema-aware tools call the output.

• Fake / dummy data — plausible-looking values generated in isolation: a name, an email, a random number. Fine for a demo; ignores your schema.
• Synthetic data — fresh rows generated from your schema: foreign keys reference real parents, and column values follow your production distributions.
• Anonymized data — real rows with sensitive fields transformed. A different job entirely: de-identifying existing data, not creating new data.

Weavori generates synthetic data. If you arrived searching for a dummy or fake data generator, the schema-driven approach is the upgrade — same one command, but the output survives a skeptical look.

Where hand-built generators break

Field-by-field generators and custom scripts fail in three ways, in order of appearance:

1. Foreign keys. Creating an order requires a user id that does not exist yet. You either insert the parent first and capture the id (the script now encodes dependency order by hand), or look it up — and when the lookup is wrong, the generator silently produces data that does not match your intent.
2. Schema drift. Add a NOT NULL column or a relation and the script stops matching the schema. Every schema change becomes a generator change, and the generator is the last file anyone updates.
3. Uniform randomness. A flat random loop is not a distribution. If 70% of your real status values are active, a uniform generator gives you roughly a third of each — and the Postgres query planner responds with different indexes, joins, and plans than production.

How Weavori generates Postgres data

Weavori reads the schema as the source of truth. The workflow is three steps:

1. Connect — point Weavori at any PostgreSQL database, or paste CREATE TABLE statements (DDL paste mode) when no database exists yet
2. Introspect — it reads tables, foreign keys, column types, constraints, and value distributions (pg_stats)
3. Generate — parents generate before children in FK dependency order, streaming rows with constant memory

The preview shows the full plan before a single row is written — estimated rows per table, generator choices, and any columns it will fall back on. Approve it and Weavori writes directly to the target database via COPY, or to stdout as JSON/CSV.

Generating Postgres data in CI

A data generator belongs in the pipeline, not in a developer's shell history. In CI the requirements change: headless authentication, parseable output, deterministic exit codes, and no phone-home licensing.

Weavori supports API keys for headless auth, plain and json output, standardized exit codes for pipeline branching, and a fingerprint-based schema cache so repeat runs skip introspection. Licenses are Ed25519-signed and validate offline — CI runners on restricted networks do not phone home. See the CI/CD guide for GitHub Actions, GitLab, CircleCI, and Jenkins examples.

Related guides

• Postgres test data — the five ways to get test data, compared
• Postgres seed data generator — seeding an empty database, compared
• Postgres synthetic data — synthetic vs. mock vs. anonymized, explained
• Framework seed guides — Prisma, Drizzle, TypeORM, Django, Rails, and Laravel, the same loop in your stack
• How Weavori works — introspection, FK resolution, and distribution sampling
• Comparison hub — honest trade-offs against other tools