Free Visual Database Schema Designer

Designing a database schema is one of the most consequential decisions in any software project. The structure you choose determines query performance, data integrity, and how easily your application can evolve. Our visual schema designer makes this process intuitive — design your database by dragging and connecting tables on a canvas, then export production-ready SQL for your database of choice.

Visual Database Design vs. Writing SQL by Hand

Writing CREATE TABLE statements by hand works fine for small schemas with a few tables. But once your schema grows beyond half a dozen tables with foreign key relationships, the purely text-based approach starts to break down. You lose the ability to see the big picture — how tables relate to each other, where the many-to-many junctions sit, which tables are central to the schema and which are peripheral. You also lose the ability to quickly experiment with structural changes, because every rename or type change requires updating multiple foreign key references manually.

Visual design solves these problems by showing your schema as an entity-relationship diagram where you can see every table, column, and relationship at a glance. Dragging a relationship line between two tables automatically creates the foreign key column with the correct type. Renaming a column that is referenced by foreign keys can propagate the change. And the spatial layout itself communicates information — closely related tables can be grouped together, making the schema's architecture visible to anyone reviewing it.

Our designer gives you the best of both worlds: a visual canvas for design and exploration, a text DSL for fast editing, and raw SQL for export and import. All three modes stay synchronized, so you can switch between them freely based on what you are doing. Use the diagram to design and communicate; use the DSL to make bulk changes quickly; use the SQL to integrate with your existing workflow.

Database Normalization Explained: 1NF, 2NF, 3NF

Normalization is the process of organizing a database schema to reduce redundancy and prevent update anomalies. There are several normal forms, each building on the previous one. Most production schemas aim for Third Normal Form (3NF), which eliminates the most common types of data duplication.

First Normal Form (1NF) requires that every column contains atomic (indivisible) values and that there are no repeating groups. A table that stores multiple phone numbers in a single comma-separated column violates 1NF. The fix is to create a separate phone_numbers table with one row per phone number, linked to the user by a foreign key. Similarly, columns like tag1, tag2, tag3 violate 1NF — use a junction table instead.

Second Normal Form (2NF) requires 1NF plus the elimination of partial dependencies — every non-key column must depend on the entire primary key, not just part of it. This matters most for tables with composite primary keys. Consider an order_items table with a composite key of (order_id, product_id). If the table includes a product_name column, that column depends only on product_id, not on the full composite key. The fix is to move product_name to the products table, where it depends on that table's primary key.

Third Normal Form (3NF) requires 2NF plus the elimination of transitive dependencies — non-key columns must not depend on other non-key columns. If your employees table has department_id, department_name, and department_location, the name and location depend on department_id, not on the employee. Moving department details to a separate departments table eliminates this transitive dependency. Now, renaming a department requires updating exactly one row instead of every employee in that department.

Normalization is not always the right choice. Read-heavy analytical workloads sometimes benefit from denormalized schemas that store pre-joined data to avoid expensive joins at query time. But for transactional applications where data integrity matters, 3NF is the right starting point. You can always denormalize later with materialized views or caching layers; recovering from a denormalized schema riddled with data inconsistencies is much harder.

Choosing the Right Data Types

Data types are not interchangeable. Storing a date as a VARCHAR means the database cannot enforce date validity, cannot sort chronologically without a cast, and cannot use date-specific functions like extracting the month or calculating the difference between two dates. Storing a boolean as an INT means any integer value is accepted, not just 0 and 1. Storing a price as a FLOAT introduces floating-point rounding errors — 0.1 + 0.2 does not equal 0.3 in IEEE 754 arithmetic.

Here are the most common type decisions and the right choice for each:

Monetary values: Use DECIMAL(10,2) or NUMERIC, never FLOAT or DOUBLE. Exact decimal arithmetic prevents rounding errors in financial calculations.
Timestamps: Use TIMESTAMP WITH TIME ZONE (PostgreSQL) or DATETIME (MySQL/SQL Server). Always store times in UTC and convert to local time zones in the application layer.
Identifiers: Use UUID if you need globally unique IDs that can be generated by any client without coordination. Use auto-incrementing INTEGER or BIGINT if you want compact, sequential IDs and your application architecture supports centralized ID generation.
Short strings: Use VARCHAR(n) with a reasonable length limit. The limit serves as documentation and a safety net against unexpectedly long data.
Long text: Use TEXT for unbounded content like comments, descriptions, or article bodies. There is no performance difference between VARCHAR(10000) and TEXT in most databases.
Booleans: Use BOOLEAN (PostgreSQL/SQLite) or BIT (SQL Server). MySQL maps BOOLEAN to TINYINT(1), which is functionally equivalent.

Relationship Types: One-to-One, One-to-Many, Many-to-Many

One-to-many is the most common relationship type. One customer has many orders. One author has many posts. One department has many employees. You implement this by adding a foreign key column to the "many" side: orders.customer_id references customers.id. Each order belongs to exactly one customer, but each customer can have any number of orders.

One-to-one relationships are less common but useful for splitting a large table or separating optional data. A users table might have a one-to-one relationship with a user_profiles table that stores optional details like biography and avatar. You implement this by adding a foreign key with a UNIQUE constraint: user_profiles.user_id references users.id and is declared UNIQUE, ensuring each user has at most one profile.

Many-to-many relationships require a junction table (also called a join table or bridge table). A blog post can have many tags, and each tag can be applied to many posts. You cannot represent this with a single foreign key in either table. Instead, create a post_tags table with two foreign key columns: post_id and tag_id. Each row in the junction table represents one post-tag association. The composite of both columns should be declared as a unique constraint or primary key to prevent duplicate assignments.

Our schema designer makes creating relationships intuitive. In diagram mode, drag a connection line from one table to another and specify the relationship type. The designer automatically creates the foreign key column with the correct type matching the referenced primary key, and generates the appropriate constraint syntax for your chosen database dialect.

Three Ways to Design

The designer supports three editing modes that stay synchronized, so you can switch between them freely:

Diagram mode. The visual canvas where you see tables as nodes connected by relationship lines. Click any table to edit its columns, types, and constraints in the side panel. Drag tables to arrange them in a layout that communicates your schema's architecture.
DSL mode. A lightweight text syntax inspired by DBML that lets you define tables and relationships with minimal typing. Changes in the DSL are reflected in the diagram when you switch back.
SQL mode. View the generated CREATE TABLE statements for your selected database dialect, or paste existing SQL to import an existing schema. The SQL output is properly ordered — tables with foreign key dependencies are created after the tables they reference.

Multi-Dialect SQL Generation

Our designer generates correct SQL for four major database systems, handling syntax differences automatically:

PostgreSQL — Uses SERIAL for auto-increment, BOOLEAN type, TIMESTAMP WITH TIME ZONE, and standard SQL foreign key syntax.
MySQL — Uses AUTO_INCREMENT, TINYINT(1) for booleans, backtick quoting, and InnoDB-compatible foreign key declarations.
SQLite — Maps types to SQLite's type affinity system (INTEGER, TEXT, REAL, BLOB), uses INTEGER PRIMARY KEY for auto-increment.
SQL Server — Uses IDENTITY(1,1) for auto-increment, BIT for booleans, NVARCHAR for Unicode strings, and bracket quoting.

Step-by-Step: Designing a Schema with This Tool

Start with a template or blank canvas. Load a starter template (Blog, E-commerce, SaaS, Social Network) to see how a well-designed schema is structured, or start fresh with an empty canvas.
Add tables. Click "Add Table" to create a new entity. Give it a descriptive singular name (e.g., user, order, product). The designer automatically creates an id primary key column.
Define columns. Click a table to open the column editor. Add columns with appropriate names, types, and constraints (NOT NULL, UNIQUE, DEFAULT). Think about what data types are correct for each field.
Create relationships. Drag a connection from one table to another. Specify the relationship type (one-to-many, one-to-one) and which columns are involved. The foreign key column is created automatically.
Review and export. Switch to SQL mode to review the generated DDL. Select your target database dialect, copy the SQL, and run it in your database client.

Related Tools

Have existing SQL and just want to visualize it? Try our SQL to Diagram tool — paste your DDL and get an instant visual representation. Need to compare two versions of a schema? Use our Schema Diff tool to see what changed and generate migration scripts. For API-level schema work, see our JSON Schema Validator and Mock Data Generator. To learn more, read our Database Design Fundamentals guide.

Frequently Asked Questions

Is my schema sent to a server?

No. The entire designer runs in your browser. Your tables, columns, relationships, and generated SQL never leave your device. This is especially important when working with proprietary or production database schemas.

Can I import an existing database schema?

Yes. Switch to SQL mode and paste your CREATE TABLE statements. The parser handles MySQL, PostgreSQL, SQLite, and SQL Server syntax, extracting table names, column definitions, data types, constraints, and foreign key relationships. The visual diagram is generated automatically from the parsed SQL.

How do I create a many-to-many relationship?

Create a junction table (e.g., post_tags) with two foreign key columns, one referencing each of the related tables. Then create one-to-many relationships from each parent table to the junction table. The junction table's primary key should be the composite of both foreign key columns.

Which database dialect should I choose?

Choose the dialect that matches your production database. If you are starting a new project and have not decided, PostgreSQL is the most feature-rich open-source option. MySQL is widely used in web applications. SQLite is ideal for embedded applications and local development. SQL Server is common in enterprise environments.

Can I export the diagram as an image?

The visual diagram can be captured using your browser's built-in screenshot tools or a screenshot extension. The diagram is rendered as interactive HTML elements rather than a static image, which means it stays crisp at any zoom level.