VS Code VS DDL to Data
Compare VS Code VS DDL to Data and see what are their differences
Shared cloud environments for AI coding agents. Run Claude Code, Cursor CLI, Codex, and Gemini CLI from any device, API, or automation tool.
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VS Code
Sublime Text
Vim
Node.js
Notepad++
Microsoft Visual Studio
GitHub
IntelliJ IDEA
DDL to Data
Mockaroo
Generate Data
Snaplet
Random Data
TestDataHub
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 detectionโcolumn 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.
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DDL to Data's answer:
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.
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.
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.
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
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.
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.
Based on our record, VS Code seems to be more popular. It has been mentiond 1215 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Visual Studio Code, a code editor created by Microsoft, was first introduced on April 29, 2015, at the Build conference. - Source: dev.to / 28 days ago
The step up from there is an editor with a built-in agent like Cursor, Google Antigravity, Windsurf, or VS Code with a coding extension. These are code editors with an AI agent living inside them, and the difference is the responsible party for getting things from place to place. Instead of the software creator shuttling code between windows, the AI agent edits the project files directly and runs the GitHub and... - Source: dev.to / about 1 month ago
For IDE-heavy teams, BYOK (bring your own key) can be interesting, no matter whether you live in WebStorm or VS Code. On the JetBrains side, the JetBrains AI plans and Junie BYOK docs allow it, and most VS Code AI extensions offer the same idea: keep the IDE, connect provider keys, pay the provider. - Source: dev.to / 2 months ago
Option 1: Raw editing in IDE. You open the .md file in VS Code or whatever you use. Syntax highlighting shows you the structure. Maybe you toggle a preview pane. This works for quick edits but becomes painful for anything involving tables, diagrams, or complex formatting. - Source: dev.to / 2 months ago
You'll need Python 3.8+ and pip for the quickstart, with venv recommended for isolation. Install the requests library for HTTP calls. VS Code with the Python extension works well as an editor, though PyCharm or Sublime Text work equally well. You'll also need a free Foxit developer account. - Source: dev.to / 2 months ago
Sublime Text - Sublime Text is a sophisticated text editor for code, html and prose - any kind of text file. You'll love the slick user interface and extraordinary features. Fully customizable with macros, and syntax highlighting for most major languages.
Mockaroo - A realistic data generator to test your app
Vim - Highly configurable text editor built to enable efficient text editing
Generate Data - GenerateData.com: free, GNU-licensed, random custom data generator for testing software
Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
Snaplet - Snaplet gives developers production-accurate data and preview databases they can code against, so they can focus on shipping. Ditch the seed script forever!