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Superset: Data Insights

Name: Superset: Data Insights
Author: apache

Get data visualization and exploration with Superset, a platform for founders in data-driven startups, with 74k+ GitHub stars.

74,301 stars18,124 forksPythonHealth Score 8/10Updated 8/19/2026100% free · open source

What it does

Apache Superset is a data visualization and exploration platform that helps founders in data-driven startups to easily create dashboards and explore their data

Install / run

docker-compose up

When to use it

•When you need to create interactive dashboards to visualize your startup's key metrics
•When you want to explore and analyze your data without writing complex SQL queries
•When you need to share data insights with your team or stakeholders in a easy-to-understand format

Quick start

1Clone the Superset repository from GitHub using the command `git clone https://github.com/apache/superset`
2Create a new virtual environment using `python -m venv venv` and activate it with `source venv/bin/activate`
3Initialize the Superset database using `superset db upgrade`
4Start the Superset development server using `superset run -p 8088 --with-exampledatas`
5Access the Superset web interface at `http://localhost:8088` and log in with the default credentials `admin/admin`

Ready-to-paste prompt

superset run -p 8088 --with-exampledatas --username admin --password admin

Heads up: You need to have Docker and Docker Compose installed on your system to run Superset using the `docker-compose up` command, and also ensure you have the necessary dependencies installed, including Python 3.7 or higher

Saves to your device

Topics

analytics

apache

apache-superset

asf

business-analytics

business-intelligence

data-analysis

data-analytics

data-engineering

data-science

data-visualization

data-viz

flask

python

react

sql-editor

superset

How Superset: Data Insights works

Codeflow

Free to inspect

Superset: Data Insights is a very large Python project (~812k lines across 5099 code files, plus 1522 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, Apache-2.0 license, has a test suite.

Size

Very large codebase

~812k lines · 5099 code files · days to read — use, don't read

Setup

Developer setup

A real software project. Use it via its install path; don't expect to read it all.

Runs on

Python · Docker

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