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ยท 3 min read

Having a visual representation of your data model can be helpful as it allows for easier comprehension of complex data structures, and enables developers to better understand and collaborate on the data model of the application they are building.

Entity relationship diagrams (ERDs) are a common way to visualize data models, by showing how data is stored in the database. ERDs are graphical representations of the entities, their attributes, and the way these entities are related to each other.

Today we are happy to announce the release of DjangoViz, a new tool for automatically creating ERDs from Django data models.

Django is an open source Python framework for building web applications quickly and efficiently. In this blog post, I will introduce DjangoViz and demonstrate how to use it for generating Django schema visualizations using the Atlas playground.


Django ORMโ€‹

Django ORM is a built-in module in the Django web framework. It offers a high-level abstraction layer that enables developers to define complex application data models with ease. Unlike traditional ORM frameworks that rely on tables and foreign keys, Django models are defined using Python objects and relationships:

from django.db import models

class User(models.Model):
username = models.CharField(max_length=255)
email = models.EmailField(unique=True)
password = models.CharField(max_length=255)

class Post(models.Model):
title = models.CharField(max_length=255)
content = models.TextField()
author = models.ForeignKey(User, on_delete=models.CASCADE)

When the application runs, Django translates these Python models into database schemas, mapping each model to a corresponding database table and each field to a corresponding column in the table.
When working with schemas and making changes to them, being able to understand the full picture just through code can get complicated very quickly. To help developers better understand their schema, we have created DjangoViz.

Introducing DjangoVizโ€‹

For the purpose of this demo, we will follow the Django getting started tutorial, and showcase how you can use DjangoViz to visualize the default models included by Django's startproject command.

First, install Django and create a new project:

pip install Django
django-admin startproject atlas_demo
cd atlas_demo

Install the DjangoViz package:

pip install djangoviz

Add DjangoViz to your Django project's INSTALLED_APPS in atlas_demo/


DjangoViz supports either PostgreSQL or MySQL, in this example we will use PostgreSQL:

Install the PostgreSQL driver:

pip install psycopg2-binary

Configure the database to work with PostgreSQL in the file:

"default": {
"ENGINE": "django.db.backends.postgresql_psycopg2",
"NAME": "postgres",
"USER": "postgres",
"PASSWORD": "pass",
"HOST": "",
"PORT": "5432",

Start a PostgreSQL container:

docker run --rm -p 5432:5432  -e POSTGRES_PASSWORD=pass -d postgres:15

Now, you can visualize your schema by running the djangoviz management command from your new project directory:

python djangoviz

You will get a public link to your visualization, which will present an ERD and the schema itself in SQL or HCL:

Here is a public link to your schema visualization:

When clicking on the link you will see the ERD of your new project:


Wrapping upโ€‹

In this post, we discussed DjangoViz, a new tool that helps to quickly visualize Django schemas. With this tool, you can easily get an overview of the data model and visual of your schema. We would love to hear your thoughts and feedback if you decide to give it a go!

Have questions? Feedback? Find our team on our Discord server โค๏ธ.

ยท 4 min read
Rotem Tamir

Atlas is most commonly used for managing and applying schema changes to databases, but it can also be used for something else: exploring and understanding database schemas.

With inspection, Atlas connects to your database, analyzes its structure from the metadata tables, and creates a graph data structure that maps all the entities and relations within the database. Atlas can then take this graph and represent it in various formats for users to consume. In this post, I will present two such forms of representation: Entity Relationship Diagrams (ERDs) and JSON documents.

Schemas as ERDsโ€‹

One of the most useful ways to represent a database schema is using an Entity Relationship Diagram (ERD). This allows developers to see the schema in a visual and intuitive way, making it easy to understand the relationships between different elements of the database. When using ERDs, because the data is presented in a graph format, you can easily navigate through the schema and see how different entities are connected. This can be especially useful when working with complex or large databases, as it allows you to quickly identify patterns and connections that might not be immediately obvious when looking at the raw data.

Using Explore to generate an ERD

To automatically generate an ERD from your database, you can use the Explore feature of Atlas Cloud. To visualize a schema using the Explore feature, you need to provide your database schema in one of two ways:

  1. Provide a connection string to your database. This will allow Atlas Cloud to connect to your database and automatically generate a schema from the metadata tables. Note: this method only works for databases that are publicly accessible via the internet.

  2. Provide the schema as an Atlas HCL file. If you have an existing Atlas project, you can use the atlas schema inspect command to generate the HCL file from your database.

    After installing Atlas, you can run the following command to generate the HCL representation of your database schema:

    # MySQL
    atlas schema inspect -u mysql://root:pass@localhost:3306/db_name

    # PostgreSQL
    atlas schema inspect postgres://postgres:pass@localhost:5432/db_name?sslmode=disable

Schemas as JSON documentsโ€‹

In addition to producing ERDs, Atlas can also produce a JSON document that represents the database schema. One of the key benefits of representing the database schema as a JSON document is that it allows you to use standard tools like jq to analyze the schema programmatically. jq is a popular command-line tool for working with JSON data, and it can be especially useful for exploring and manipulating the schema data generated by Atlas.

With jq, you can easily extract specific information from the schema, such as the names of all the tables in the database or the foreign key relationships between different entities. This makes it easy to write scripts or programs that can automatically analyze the schema and identify potential issues or opportunities for optimization.

To get the JSON representation of your database schema, you can use the atlas schema inspect command with a custom logging format:

atlas schema inspect -u '<url>' --format '{{ json . }}'

This will output the schema as a JSON document:

"schemas": [
"name": "test",
"tables": [
"name": "blog_posts",
"columns": [
"name": "id",
"type": "int"
"name": "title",
"type": "varchar(100)",
"null": true
// .. Truncated for brevity ..

Once your schema is represented as a JSON document, you can use jq to analyze it. For example, to get a list of all the tables that contain a foreign key, run:

atlas schema inspect -u '<url>' --format '{{ json . }}' | jq '.schemas[].tables[] | select(.foreign_keys | length > 0) | .name'

This will output:


Wrapping upโ€‹

In this blog post, we demonstrated how Atlas can be used as a schema inspection and visualization tool, in addition to its more commonly known use as a schema migration tool. We showed how to use the Explore feature to create an ERD from your database schema, and how to use the atlas schema inspect command to generate a JSON document that can be analyzed using jq and other tools.

Have questions? Feedback? Feel free to reach out on our Discord server.