Data Model Generator
Build structured data models from a quick description
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Which comes first, the tables or the thinking? Most projects start with a migration file and find the missing relationship three weeks later, once real data is in there. The Data Model Generator gives you the step in between: a written model of your entities, their fields and how they connect, built from plain English before anyone writes a CREATE TABLE statement.
Data Model Generator suits the hour before anyone writes a migration, while the entities are still arguable. Describe what your application stores and it returns the entities, fields, keys and relationships as a structure you can hand to someone else to pull apart.
What is Data Model Generator?
Data Model Generator reads a description of what your application stores and returns the structure behind it. Sentences about who owns what become foreign keys. A rule such as one email per account becomes a constraint. You get the shape and the reasoning, not just SQL.
Entities and fields
Everything your system stores comes back as a named entity with typed fields.
Keys and relationships
One to many and many to many links are spelled out, with any join tables they need.
Rules kept as constraints
Add Constraints is on by default, so unique and required fields survive into the model.
Who Needs The Model Before The Tables
A data model is the argument you have before writing code. Several people need to be in it:
- Backend developers sketching a feature that touches four tables
- Product owners checking the plan holds up before it becomes a build
- Students learning normalisation with nobody around to mark the work
- Analysts meeting an unfamiliar system and needing its shape fast
How Does Data Model Generator Work?
Describe what your system stores, then let two settings do the work, because the panel is shared across the database tools. Set Output Type to Data Model, or you may get a single query back. Set Format to Explained for reasoning beside the structure, or Table when you want it scannable.
Database narrows the syntax to PostgreSQL or whichever engine you are on, and Auto is fine while that choice is open. Complexity starts on Standard, and Advanced suits a design where relationships pile up. Include Indexes and Add Constraints are already on, and Include Sample Data is off until you want rows to reason about.
Important Nothing connects to your database. The tool never reads an existing schema, so a model for a live system is only as accurate as the description you paste in.
A Booking App In Four Sentences
Say you write this: studios rent rooms by the hour, customers book a slot, a booking can be cancelled once, and prices change by day of week. What comes back opens like this:
customers (id, email UNIQUE, created_at)
rooms (id, studio_id FK, capacity, hourly_rate)
bookings (id, room_id FK, customer_id FK, starts_at, status)The reasoning underneath it is the part worth reading:
| What you wrote | What it became |
|---|---|
| A booking can be cancelled once | A status field plus a cancelled_at timestamp |
| Prices change by day of week | A separate rate table, not a column |
| Customers book a slot | A foreign key from bookings to customers |
What It Cannot Know About Your Data
The model is a proposal. It has no idea how many rows you expect, how often a field is read, or which report runs every Monday. Those decide indexing and denormalisation more than the shape does. Check the generated types against your real ranges, and show the design to someone who knows the domain while changing it is free.
Each job here gets its own free tool on EizTools rather than one chat box under many names, and nothing asks you to sign in. Once your entities settle, the queries against them are the next job, and the SQL Generator turns a plain English request into SQL. Pick a model from the selector, generate, and keep the result open while you work.
Frequently Asked Questions
Does the Data Model Generator connect to my database?
No. It never touches a live system. You describe what you store and it writes the model back as text, so an existing schema has to be pasted in if you want it taken into account.
Does it work for a document database?
Yes. Set Database to MongoDB and the model comes back as collections and embedded documents rather than join tables. Leave it on Auto while the engine is undecided, since the shape of the data matters more than the syntax at that stage.
Can I get a migration out of it?
Yes, by changing Output Type, since Schema and Migration sit in the same dropdown as Data Model. Run any migration against a copy of your data first, because nothing here executes.
How detailed should my description be?
Enough to name everything you store and how the pieces relate. One sentence per entity, plus any rule about uniqueness or ownership, gets a usable model. Vague input produces vague field names.
A data model written down is far cheaper to argue with than a migration already applied. Describe your system in the Data Model Generator above, read what comes back slowly, then change the parts that do not match how your users actually behave.
Sabir Bepari
Founder of EizTools and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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