MockBlast
Generate 1M+ rows of SQL/Mongo data instantly & fast.
What it does
Generate millions of rows of mock data for PostgreSQL, MySQL, MongoDB, & SQLite. Stop writing INSERT scripts manually. MockBlast parses your raw CREATE TABLE schema to auto-detect types (UUID, JSONB, Boolean). Unlike basic tools, it maintains Referential Integrity (Foreign Keys) across tables and generates valid Bcrypt passwords for testing logins. Built with streaming technology to generate 1M+ rows instantly. 10,000 rows free (10x the industry standard).
Does a similar job
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- ATA tool to seed your dev database with real data2022 · ▲129
A bunch of developers and myself have created RepliByte - an open-source tool to seed a development database from a production database. Features: - Support data backup and restore for PostgreSQL, MySQL and MongoDB - Replace sensitive data with fake data - Works on large database (> 10GB) (read Design) - Database Subsetting: Scale down a production database to a more reasonable size - Start a local database with the prod data in a single command - On-the-fly data (de)compression (Zlib) - On-the-fly data de/encryption (AES-256) - Fully stateless (no server, no daemon) and lightweight…
- STSupabase-Test – Fast Isolated Postgres DBs for Testing Supabase RLSNov 2025 · npmjs.com · ▲30
Hi HN — we've built a testing framework for Supabase that spins up fast, isolated Postgres databases for each test case. It’s designed to make RLS policies easy to validate with real database state, without global test fixtures or mock auth. Features: - Instant isolated Postgres DBs per test - Automatic rollback after each test - RLS-native testing with `.setContext()` for auth simulation - Flexible seeding (SQL, CSV, JSON, JS) - Works with Jest, Mocha, and any async test runner - CI-friendly (runs cleanly in GitHub Actions) We also published example projects and a free set of tutorials:…
- DTDDL to Data – Generate realistic test data from SQL schemasJan 2026 · ▲55
I built DDL to Data after repeatedly pushing back on "just use production data and mask it" requests. Teams needed populated databases for testing, but pulling prod meant security reviews, PII scrubbing, and DevOps tickets. Hand-written seed scripts were the alternative slow, fragile, and out of sync the moment schemas changed. Paste your CREATE TABLE statements, get realistic test data back. It parses your schema, preserves foreign key relationships, and generates data that looks real, emails look like emails, timestamps are reasonable, uniqueness constraints are honored. No setup, no…
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