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DDL to Data

Turn SQL schemas into realistic test data in seconds. Perfect for testing, demos, and development.

DDL to Data is a developer tool that automatically generates realistic test data from SQL schemas. Simply paste your CREATE TABLE statement and get back JSON data with smart type detectionโ€”column names like "email" produce real email formats, "phone" produces phone numbers, etc. It supports PostgreSQL, MySQL, and SQLite, handles foreign key relationships for referentially-intact data, and integrates easily into CI/CD pipelines via REST API. Ideal for database testing, seeding dev environments, creating demo data, and automated test pipelines.

A startup from Tampa, the United States that is founded by Travis.

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DDL to Data Reviews and Details

This page is designed to help you find out whether DDL to Data is good and if it is the right choice for you.

#Developer Tools #API Tools #QA Tools #Testing #Test Data Generator

Screenshots and images

  • DDL to Data Demo
    Demo //
    2026-01-01
  • DDL to Data Data Formats
    Data Formats //
    2026-01-01

Features & Specs

  1. Smart Type Detection

    Generates realistic data based on column names (email, phone, address, etc.)

  2. Database Support

    PostgreSQL, MySQL, SQLite

  3. Foreign Key Support

    Generates consistent, referentially-intact relational data

  4. Response Format

    JSON, SQL, CSV, PARQUET, XLSX

  5. Free Tier

    100 API calls + 5,000 rows/month

  6. Schema Storage

    Save and reuse schemas for repeat generation

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Questions & Answers

As answered by people managing DDL to Data.

  1. What makes DDL to Data unique?

    No LLM, no prompts, no AI costs. DDL to Data uses deterministic pattern-matching โ€” not machine learning โ€” to generate realistic test data from your SQL schema in milliseconds. It's fast, predictable, and won't hallucinate. Column named "email" produces an email, "phone" produces a phone number. Same schema, same structure, every time. Plus it handles foreign key relationships to generate referentially-intact data across multiple tables.

  2. Why should a person choose DDL to Data over its competitors?

    Unlike AI-powered tools, DDL to Data has zero token costs, sub-second response times, and deterministic output, critical for CI/CD pipelines. Unlike Faker libraries, it requires zero configuration: paste your CREATE TABLE and get intelligent, type-aware data without writing any setup code. It also supports multiple output formats (JSON, CSV, SQL, Parquet, Excel) and can seed data directly into your PostgreSQL database.

  3. How would you describe the primary audience of DDL to Data?

    Backend developers, QA engineers, and DevOps teams who need realistic test data for database testing, seeding dev environments, CI/CD pipelines, and product demos. Particularly useful for teams who want a reliable, no-config utility that just works, without adding AI dependencies to their infrastructure.

  4. What's the story behind DDL to Data?

    Every new project meant the same tedious ritual: write the schema, then manually create arrays of fake emails, phone numbers, and timestamps. Over and over. It struck me that the schema already contains everything needed to generate realistic data, column names are semantic. "email" means email, "created_at" means timestamp. So I built an API that does the obvious thing automatically, without any AI complexity

  5. Which are the primary technologies used for building DDL to Data?

    FastAPI (Python) backend with PostgreSQL and SQLAlchemy. Next.js 14 frontend with TypeScript and Tailwind CSS. Hosted on AWS with Docker containers, and CircleCI for CI/CD.

  6. Who are some of the biggest customers of DDL to Data?

    Currently in public beta and growing organically. Early adopters include indie developers and small engineering teams using it for local development and automated testing pipelines.

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Is DDL to Data good?

I don't have verified, up-to-date information about ddltodata.com specifically, so I can't confirm its quality, reliability, or legitimacy. Based on the name, it appears to be a tool that generates sample/mock data from DDL (Data Definition Language) schema statements, but I have no firsthand or sourced data on user reviews, security practices, or company reputation for this exact site.

Why choose DDL to Data?

  • Tools that convert DDL schemas into sample data can be useful for quickly populating test databases without manual data entry
  • If it works as implied by the name, it could save developers time during testing, prototyping, or QA environments
  • Such tools often support multiple SQL dialects (e.g., MySQL, PostgreSQL, SQL Server), which can be convenient for cross-platform projects
  • Web-based converters typically don't require software installation, lowering the barrier to entry for quick tasks

Recommended for

  • Developers or QA testers needing to quickly generate mock data for database testing
  • Small teams prototyping database schemas who want sample data without writing custom scripts
  • Users who have verified the site's legitimacy and reviewed its privacy/security policies for handling any schema information
  • Anyone willing to independently confirm the tool's accuracy and data handling practices before uploading real schema files

External links

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  • Public traffic stats of DDL to Data

    Check the traffic stats of DDL to Data on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.

  • Domain Rating (DR)

    Check the "Domain Rating" of DDL to Data on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of DDL to Data's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.

  • Domain Authority (DA)

    Check the "Domain Authority" of DDL to Data on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.

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