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DDL to Data VS GitHub Surf

Compare DDL to Data VS GitHub Surf and see what are their differences

DDL to Data

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

Rating
0 reviews
Pricing
$19 / Monthly (500 API calls/month 50,000 rows/month)
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Alternatives Is it good?
GitHub Surf

Open repositories in a VSCode environment in your browser

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Rating
0 reviews
Pricing
Open source
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Sendaway.co
Send files and text between devices with a simple 6-digit code. No account, email or shared link required. sponsored

Which is more popular?

Testing popularity
100% vs 0%
alternatives listed
33 vs 1

Base details

Website, pricing, platforms and company facts side by side.

DDL to Data
GitHub Surf
Website ddltodata.com github.surf
Pricing
$19 / Monthly (500 API calls/month 50,000 rows/month) Official pricing
Open source
Company Startup from the United States · 2025 —
Listed in
Developer Tools API Tools QA Tools Testing Test Data Generator +2
Developer Tools Productivity Note Note Taking +1

About DDL to Data and GitHub Surf

In their own words, as submitted to SaaSHub.

DDL to Data
GitHub Surf

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 detection—column names like "email" produce real email formats, "phone" produces phone numbers,...

Read more about DDL to Data

No description of GitHub Surf yet.

Features and specs

What each product offers, as listed by its team.

DDL to Data 6 features
GitHub Surf 4 features
  • Smart Type Detection
    Generates realistic data based on column names (email, phone, address, etc.)
  • Database Support
    PostgreSQL, MySQL, SQLite
  • Foreign Key Support
    Generates consistent, referentially-intact relational data
  • Response Format
    JSON, SQL, CSV, PARQUET, XLSX
  • Free Tier
    100 API calls + 5,000 rows/month
  • Schema Storage
    Save and reuse schemas for repeat generation
  • User-Friendly Interface
    GitHub Surf provides an intuitive and easy-to-navigate interface that makes it accessible for both beginners and experienced users.
  • Integration with GitHub
    The tool seamlessly integrates with GitHub, allowing users to easily access repositories and track changes efficiently.
  • Real-Time Collaboration
    It supports real-time collaboration features, enabling multiple users to work on the same project simultaneously and see each other's edits instantly.
  • Visual Representation
    Provides a visual representation of project histories and branches, which helps in understanding the development process better.

Possible disadvantages

  • Limited Offline Capabilities
    GitHub Surf requires an internet connection for most functionalities, which limits its usability in offline scenarios.
  • Learning Curve
    Users new to GitHub or similar platforms might experience a learning curve when first using GitHub Surf, owing to its various features and tools.
  • Performance Issues
    Some users may experience performance lags or slower response times when working with large repositories.
  • Privacy Concerns
    As with any online tool, there might be concerns related to data privacy and security, especially when dealing with sensitive project information.

Analysis

An editorial look at what each product does well and who it suits.

DDL to Data
GitHub Surf

Overall verdict

  • 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 this product is good

  • 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

Overall verdict

  • GitHub Surf appears to be a niche tool designed to enhance the experience of browsing and exploring GitHub repositories, offering a more visual or streamlined interface than GitHub's native UI. It seems useful for developers who want a faster or more intuitive way to navigate codebases, though as a smaller or newer service, it may lack the robustness, feature completeness, or long-term support of GitHub's official tools.

Why this product is good

  • Provides an alternative, potentially more visual or user-friendly way to browse GitHub repositories
  • May offer quicker navigation or discovery features compared to GitHub's default interface
  • Useful for developers who want to explore codebases without cloning them locally
  • Likely lightweight and focused on a specific browsing use case

Recommended for

  • Developers who frequently browse open-source repositories and want a different visual experience
  • Users looking for quick repository exploration without needing full GitHub account features
  • Casual coders or students exploring codebases for learning purposes
  • Those seeking alternative UI/UX for GitHub navigation as a supplementary tool

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
DDL to Data
GitHub Surf
100% 100%
Testing
0% 0%
66% 66%
Developer Tools
34% 34%
0% 0%
Note Taking
100% 100%
100% 100%
Test Data Generator
0% 0%

Questions & Answers

As answered by people managing DDL to Data and GitHub Surf.

What makes your product unique?

DDL to Data's answer

No LLM, no prompts, no AI costs. DDL to Data uses deterministic pattern-matching — not machine learning — 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.

Why should a person choose your product over its competitors?

DDL to Data's answer

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.

How would you describe the primary audience of your product?

DDL to Data's answer

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.

What's the story behind your product?

DDL to Data's answer

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

Which are the primary technologies used for building your product?

DDL to Data's answer

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.

Who are some of the biggest customers of your product?

DDL to Data's answer

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.

User comments

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Alternatives to DDL to Data and GitHub Surf

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