Introduction

A few years ago, I spent days trying to debug a report that kept giving us the wrong numbers. The queries were fine. The database was live. But something felt off. Turns out, the data model was a mess. Tables were duplicated. Relationships were missing. Some columns had three different names depending on where you looked.

That moment stuck with me.

As someone who's built internal data tools at companies like Fiverr, I’ve seen what poor structure can cost. It wastes time, stalls growth, and turns smart teams into spreadsheet firefighters.

That’s part of why we built ChartDB - a simple, visual way to model your SQL database and make sure the structure never gets in your way.

If you’ve ever had to guess what a column really means or why a join doesn’t return the right result, this post is for you.

What Is Data Modeling in SQL?

Data modeling is the process of organizing and structuring your data in a SQL database. Think of it like planning a building before laying bricks. It defines what tables exist, what each one stores, and how they connect.

The goals are simple:
• Keep your data consistent
• Make it easy to work with
• Help your system grow without breaking

You’ll hear three core pieces show up in every model:
• Entities (the tables like users or orders)
• Attributes (the columns like email or total_amount)
• Relationships (foreign keys that tie tables together)

A solid data model brings all three into harmony.

Why Data Modeling Matters

Good data modeling saves time, reduces bugs, and keeps everyone aligned.

From a business view, it means:
• Your reports match reality
• You avoid repeated work
• Teams can make decisions faster

From a technical view, it means:
• Queries run faster
• Fewer messy joins
• Less confusion for engineers and analysts

Skip this step, and you’ll feel it:
• Duplicate data that never quite syncs
• Conflicting KPIs across dashboards
• Dashboards that take minutes to load
• Endless migrations that could’ve been avoided

It’s like skipping the blueprint and wondering why the plumbing leaks.

The 3 Types of Data Models

Data modeling usually happens at three levels. Each one adds more detail. Together, they take you from idea to production.

1. Conceptual Model

This is your 10,000-foot view.

It answers: What are the main things we’re storing, and how are they connected?

At this stage, you might sketch something like:

• A Customer places one or more Orders
• Each Order contains one or more Products

No data types. No primary keys. Just big ideas.

This is the kind of thing you might draw on a whiteboard during a meeting.

2. Logical Model

Now we get into the structure.

We define:

• Tables like customers, orders, products
• Primary keys (customer_id, order_id)
• Foreign keys (like orders.customer_id)
• Relationships and cardinality (one-to-many, many-to-many)

It’s still platform-agnostic. You’re not deciding on VARCHAR(255) yet.

This is where the schema really starts to take shape.

3. Physical Model

This is the implementation. Everything becomes SQL-ready.

Here you define:

• Table and column names
• Data types (VARCHAR, INTEGER, DECIMAL)
• Indexes
• Constraints
• Naming conventions that match your DB engine

If you’re using PostgreSQL, this might be the point where you say “let’s avoid using order as a table name.”

This model gets translated into your actual database schema.

How to Use ChartDB for Data Modeling

ChartDB was built to make this process way simpler.

Here’s how:

1. Import your schema

Run one command. Paste the output. You’ll get an instant ER diagram - no setup, no credentials.

👉 Try it here: https://chartdb.io

2. See relationships right away

We auto-detect foreign keys and draw out the links between tables. You can instantly spot missing connections or surprises in your schema.

3. Edit visually

Add tables, columns, or relationships with a click. No need to write SQL just to plan things out.

4. Stay organized

Use areas to group tables. Annotate your schema. Export images or share it with your team.

ChartDB helps you go from chaos to clarity in minutes. And if you’re onboarding someone new, there’s nothing better than giving them a diagram instead of a data dump.

A video walkthrough is coming soon to show this in action.

Conclusion

That broken report I mentioned earlier? It took days to untangle. But once we sat down and re-modeled the data from scratch, everything changed. Queries got faster. Reports lined up. The team stopped arguing over which column was the right one.

If there’s one takeaway, it’s this:

Better models lead to better decisions.

Poor data modeling costs more than just performance. It creates confusion, kills trust, and slows you down.

You don’t need to accept that as normal. Start modeling your SQL database with ChartDB today. It’s fast, visual, and free to try.

Additional Resources
• How To Generate ERD from SQL
• Better Organize Your Database Schema Diagrams using ChartDB
• Know Everything About Database Relationships