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Development & Technical

Database Schema Designer

Design optimized database schemas with tables, relationships, indexes, constraints, and migration scripts for PostgreSQL, MySQL, MongoDB, and more.

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The Database Schema Designer translates your application requirements into production-ready database schemas. Describe your data models and relationships, choose your database engine, and get complete DDL scripts with tables, columns, data types, primary/foreign keys, indexes, constraints, and migration files. Supports relational (PostgreSQL, MySQL, SQLite), document (MongoDB, DynamoDB), and key-value (Redis) databases with normalized, denormalized, and star schema designs.

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Developers

E-commerce store schema in PostgreSQL

Backend developers get a normalized, production-ready DDL baseline so they can start building APIs instead of debating table structure.

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Input

Requirements
E-commerce platform: users register and place multiple orders. Each order has line items linked to products. Products belong to one or more categories and support variants (size, color) with independent SKUs and stock. Users leave verified reviews with a 1-5 star rating and optional photos. Track inventory levels per variant and order status history.
Database Type
postgresql
Schema Style
normalized
Output Format
sql-ddl
Relationships
many-to-many

Output (excerpt)

CREATE TABLE users (id BIGSERIAL PRIMARY KEY, email CITEXT UNIQUE NOT NULL, created_at TIMESTAMPTZ DEFAULT now());
CREATE TABLE products (id BIGSERIAL PRIMARY KEY, name TEXT NOT NULL, base_price NUMERIC(10,2));
CREATE TABLE product_variants (id BIGSERIAL PRIMARY KEY, product_id BIGINT REFERENCES products(id), sku TEXT UNIQUE, size TEXT, color TEXT, stock INT DEFAULT 0);
CREATE TABLE product_categories (product_id BIGINT, category_id BIGINT, PRIMARY KEY (product_id, category_id)); -- junction table for many-to-many.
Marketers

Newsletter analytics warehouse (star schema)

Marketing analysts get a query-friendly star schema that powers slice-and-dice campaign dashboards without slow joins.

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Input

Requirements
Marketing analytics warehouse for email campaigns. We need to report opens, clicks, unsubscribes, and conversions by campaign, subscriber segment, send date, and device. Fact table records each engagement event; dimensions cover campaign, subscriber, date, and channel for fast aggregation in BI dashboards.
Database Type
postgresql
Schema Style
star
Output Format
diagram
Relationships
simple

Output (excerpt)

fact_engagement (event_id, date_key FK, campaign_key FK, subscriber_key FK, channel_key FK, event_type, revenue)
  |-- dim_date (date_key, full_date, week, month, quarter, year)
  |-- dim_campaign (campaign_key, name, subject_line, send_type)
  |-- dim_subscriber (subscriber_key, segment, signup_source, country)
  |-- dim_channel (channel_key, device, client, os)
Grain: one row per subscriber engagement event. Optimized for GROUP BY rollups across any dimension.
Freelancers

Freelancer invoicing app in SQLite

Solo freelancers building their own invoicing tool get a zero-config SQLite migration they can ship in a desktop or Electron app.

See input + output preview

Input

Requirements
Lightweight invoicing app for a solo freelancer. Track clients, projects per client, time entries logged against projects, and invoices that bundle billable time entries. Each invoice has a status (draft, sent, paid, overdue) and line items. Need simple one-to-many relationships and local storage on a laptop.
Database Type
sqlite
Schema Style
normalized
Output Format
migration
Relationships
simple

Output (excerpt)

-- 0001_init.sql
CREATE TABLE clients (id INTEGER PRIMARY KEY, name TEXT NOT NULL, email TEXT);
CREATE TABLE projects (id INTEGER PRIMARY KEY, client_id INTEGER REFERENCES clients(id), name TEXT, hourly_rate REAL);
CREATE TABLE time_entries (id INTEGER PRIMARY KEY, project_id INTEGER REFERENCES projects(id), minutes INTEGER, logged_on DATE, billed INTEGER DEFAULT 0);
CREATE TABLE invoices (id INTEGER PRIMARY KEY, client_id INTEGER REFERENCES clients(id), status TEXT DEFAULT 'draft', total REAL, issued_on DATE);

Your Database Schema Designer results will appear here

Expect clean code blocks with comments, plus a short explanation of what changed.

How to Use Database Schema Designer

  1. Describe your data models in plain English — entities, attributes, and how they relate to each other.
  2. Select your target database engine for engine-specific data types, syntax, and optimizations.
  3. Choose a schema style: normalized for transactional apps, denormalized for read-heavy workloads, star schema for analytics.
  4. Pick an output format: SQL DDL for direct execution, migration scripts for version control, or JSON Schema for NoSQL.

Use Cases

1

Design a relational schema for a SaaS application with multi-tenancy support

2

Create MongoDB collection schemas with embedded documents and indexes

3

Build a star schema for a data warehouse or analytics pipeline

4

Generate migration scripts for an incremental database evolution

5

Design a DynamoDB single-table design with GSI access patterns

Tips for Best Results

  • Describe your read and write patterns in the requirements — this helps the generator choose between normalized and denormalized designs.
  • For PostgreSQL, the generator will use advanced features like JSONB columns, partial indexes, and generated columns where appropriate.
  • Request 'Migration Script' output format if you use tools like Flyway, Liquibase, Alembic, or Knex — the output includes versioned migration files.
  • Include expected data volumes in your requirements (e.g., '10M users, 500M orders') for appropriate indexing and partitioning recommendations.

Frequently Asked Questions

Can it design schemas for NoSQL databases?

Yes. For MongoDB, it generates collection schemas with embedded documents, array fields, and index definitions. For DynamoDB, it designs single-table schemas with partition/sort key strategies and Global Secondary Indexes (GSIs) based on your access patterns.

Does it handle many-to-many relationships?

Yes. Select 'Many-to-many' or 'Complex' relationship complexity. The generator creates junction/pivot tables with composite primary keys, foreign key constraints, and any additional metadata columns the relationship requires.

What is the difference between normalized and denormalized?

Normalized (3NF) eliminates data redundancy and is best for transactional applications where data integrity is critical. Denormalized duplicates some data to avoid JOINs and is best for read-heavy applications where query speed matters more than storage efficiency.

Can I use the SQL DDL output directly?

Yes. The generated SQL is valid, executable DDL for the selected database engine. Copy and paste it into your database client, migration tool, or CI/CD pipeline. Always review in a staging environment before running on production.

Does it include indexes?

Yes. The generator creates indexes based on likely query patterns: foreign key columns, frequently filtered fields, unique constraints, and composite indexes for common multi-column lookups. It also notes which indexes are essential vs. optional.

How does it handle soft deletes?

When appropriate, the generator adds a deleted_at TIMESTAMPTZ column with a partial index (WHERE deleted_at IS NULL) for PostgreSQL, ensuring soft-deleted rows do not affect query performance on active records.

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