Apache Superset – Build Stunning Dashboards with Ease

Apache Superset is a free, open-source business intelligence platform for exploring data, writing SQL and publishing interactive dashboards. This guide shows you where to get the official release, which installation method fits your setup and how to go from a blank screen to a working chart.

apachesuperset.com is an independent guide. It is not the official Apache Superset website and is not affiliated with or endorsed by the Apache Software Foundation.

OVERVIEW

What Is Apache Superset?

A modern, web-based BI platform you host yourself — built for exploring data, not just reporting on it.

What it is

Apache Superset is an open-source data exploration and visualization application that runs in your web browser. It began at Airbnb, was donated to the Apache Software Foundation and became a top-level Apache project in 2021. The code is written mainly in Python and TypeScript.

How it works

Superset does not store your business data. It connects to the databases you already use through SQLAlchemy drivers, sends queries to them and renders the results as charts. A small metadata database stores users, charts, dashboards and permissions.

What it is used for

Teams use Superset to answer questions with SQL, build charts without code, publish dashboards with filters and share them with colleagues under role-based permissions.

Who it is for

Data analysts, data engineers, product and operations teams, and companies that want a self-hosted alternative to licensed BI tools. Deploying it does require some comfort with Docker, Python or Kubernetes.

OFFICIAL SOURCES ONLY

Apache Superset Download

Superset is a server-hosted web application — there is no official Windows .exe or macOS .dmg installer. You download a source release, a PyPI package or a Docker image and run it as a service.

Latest Stable: 6.1.0

Released on GitHub on 13 May 2026. See the official changelog before upgrading.

License: Apache 2.0

Free to use, modify and redistribute, including for commercial work.

Type: Web Application

Runs on a server or container and is used through a modern web browser.

Platforms: Linux & macOS

Docker Compose suits Linux/macOS testing; Kubernetes is recommended for production.

GitHub Release

Release notes, tag and changelog for Superset 6.1.0. The best starting point for Docker Compose users, who clone the repository and check out the release tag.

View release on GitHub →

Apache Source Release

The official signed source tarball distributed by the Apache Software Foundation, with .asc signatures and SHA-512 checksums for verification.

Open downloads.apache.org →

PyPI Package

Install with pip into a Python virtual environment. Check the project’s pyproject.toml for the Python versions supported by this release.

View apache_superset on PyPI →

# Docker option: clone the official repository
git clone https://github.com/apache/superset.git

# Python option: install the PyPI package
pip install apache_superset

Prerequisites: Docker + Docker Compose + Git for the container route, or a supported Python version with build tools and OS libraries for the pip route. Native Windows installation is not officially supported.

FEATURES

Key Features of Apache Superset

Everything a modern analytics team expects from a BI platform — without a license server.

Interactive Dashboards

Arrange charts on a drag-and-drop canvas and let viewers slice data with native filters and cross-filtering.

Powerful Data Visualization

Choose from a broad library of chart types, from simple bar and line charts to maps and pivot tables.

SQL Lab

A browser-based SQL editor with a schema browser, query history and the option to save results as datasets.

Database Connectivity

Connect any SQL database that offers a SQLAlchemy dialect and Python DB-API driver.

No-Code Chart Builder

Build charts by choosing metrics, dimensions and filters — no SQL needed for everyday exploration.

Customizable Dashboards

Control layout, colors and themes; recent releases moved to a token-based theming system.

Role-Based Access Control

Assign roles and permissions to decide who can view, edit or query each resource.

Semantic Layer & Metrics

Define reusable metrics and virtual datasets so teams use the same calculations.

Open-Source Flexibility

Extend, embed and self-host under the Apache License 2.0, with a public roadmap on GitHub.

BENEFITS

Why Choose Apache Superset?

The strongest reasons teams pick Superset — and the trade-off that comes with each.

Open-Source Advantage

No license fees and no vendor lock-in. You pay for the infrastructure you run it on and the time to maintain it.

Deep Customization

Configure behavior in Python, adjust themes and add database drivers to match your stack.

Scales With Your Data

Queries run on your database, so performance depends largely on your warehouse, caching and deployment.

Fits BI Workflows

Move from ad-hoc SQL to saved datasets, charts and published dashboards inside one tool.

Fast Data Exploration

Drill into a chart, adjust filters and compare time ranges without rebuilding reports.

Built for Collaboration

Share dashboards, manage ownership and control access through roles and permissions.

BEFORE YOU INSTALL

Apache Superset System Requirements

The project does not publish fixed hardware minimums. Requirements depend on how you deploy it and how many people use it.

Operating System

Docker Compose is documented for Linux and macOS. Windows has no official support; a Linux VM or container environment is the practical route.

Docker Prerequisites

Docker Engine or Docker Desktop, the docker compose plugin (v2) and Git to clone the repository.

Python Environment

A Python version listed in the release's pyproject.toml, used inside a virtual environment. pyenv is recommended.

System Dependencies

Build tools and development headers (for example for cryptography, SSL and LDAP) required by pip installs.

Hardware

Size CPU, RAM and disk to your users and query load. Container builds and asset compilation are the heaviest local tasks.

Browser Access

Use a current version of a modern browser such as Chrome, Firefox, Edge or Safari to work with the interface.

Development vs production

Development / evaluation: Docker Compose on one machine, with the bundled PostgreSQL metadata database and sample data.

Production: a strong SECRET_KEY, an external managed metadata database, Redis for caching and async work, HTTPS, backups and ideally Kubernetes with the official Helm chart. See the Kubernetes guide.

STEP BY STEP

How to Download Apache Superset

Six steps from choosing a method to a verified download.

1. Choose an installation method

Docker Compose for a quick local trial, PyPI for a Python-managed server or Kubernetes for production.

2. Open the official source

Use GitHub, downloads.apache.org or PyPI. Avoid mirrors that repackage the software.

3. Select the release

Pick the latest stable tag (6.1.0) rather than a release candidate (rc) unless you are testing.

4. Download or clone

Clone the GitHub repository, download the source tarball or install the package with pip.

5. Verify the release

For source tarballs, check the .asc signature against the project KEYS file and compare the SHA-512 checksum.

6. Continue to installation

Follow the installation steps below and read the release's UPDATING notes before upgrading.

INSTALLATION

How to Install Apache Superset

Two verified routes from the official documentation. Both start a development-grade instance.

Option A — Docker Compose (recommended for trying Superset)

Requires Docker, Docker Compose and Git. Checking out the release tag keeps the compose files in sync with the image you start. The first run downloads images and loads example data, so it takes a while.

git clone --depth=1 https://github.com/apache/superset.git
cd superset

# Boot an official release
export TAG=6.1.0
git fetch --depth=1 origin tag $TAG
git checkout $TAG
docker compose -f docker-compose-image-tag.yml up

When the output settles, open http://localhost:8088 and sign in with the default account admin / admin. Change that password immediately. Stop the stack with docker compose down.

Option B — Python / PyPI

Use a supported Python version inside a virtual environment, and install the OS packages listed in the PyPI installation guide first.

python3 -m venv venv
. venv/bin/activate
pip install --upgrade setuptools pip
pip install apache_superset

export SUPERSET_SECRET_KEY=YOUR-SECRET-KEY   # e.g. generate with: openssl rand -base64 42
export FLASK_APP=superset

superset db upgrade
superset fab create-admin
superset load_examples        # optional sample data
superset init

# Development web server on port 8088
superset run -p 8088 --with-threads --reload

Configuration & security checklist

Set a strong, private SECRET_KEY and keep it constant — it encrypts stored credentials.
Replace default admin credentials and create named user accounts.
Use PostgreSQL or MySQL for the metadata database in real deployments.
Put Superset behind HTTPS and a reverse proxy before exposing it to a network.
Review Configuring Superset and the security documentation.

Not for production as-is

The official docs are explicit: the Docker Compose setup is not production-ready out of the box, and superset run is a development server. For production, follow the Kubernetes / Helm guide or run a proper WSGI server with your own configuration.

GETTING STARTED

How to Use Apache Superset

From first login to a shared dashboard in nine steps.

1. Open the interface

Go to http://localhost:8088 (or your server's URL) in a modern browser.

2. Sign in

3. Connect a database

Settings → Database Connections → + Database. Enter a SQLAlchemy URI or use the form.

4. Create a dataset

5. Explore the data

Open the dataset in Explore, pick dimensions, metrics, filters and time ranges.

6. Build a chart

Choose a visualization type, tune its options and save it with a clear name.

7. Create a dashboard

Add saved charts to a new dashboard and arrange them with rows, tabs and markdown.

8. Apply filters

Add native filters so viewers can narrow every chart by date, region or category.

9. Share & manage

Publish the dashboard, set owners and control access through roles.

COMPARISON

Apache Superset vs Power BI, Tableau & Metabase

A fair, feature-level comparison. Pricing changes often, so check each vendor’s site for current plans.

Open source

Apache Superset

Licensing: Apache License 2.0, free
Self-hosting: Yes — Docker, pip, Kubernetes
Setup: Moderate to complex
Visualization: Wide chart library + dashboards
SQL: Strong — full SQL Lab IDE
Customization: Very high, code-level
Ideal for: Data-savvy teams wanting control
Deployment: You run and maintain it

Commercial

Microsoft Power BI

Licensing: Proprietary; free Desktop, paid sharing
Self-hosting: Limited — Power BI Report Server
Setup: Easy on Windows
Visualization: Rich visuals and marketplace
SQL: Via Power Query / DAX, not an SQL IDE
Customization: Within Microsoft’s platform
Ideal for: Microsoft 365 organizations
Deployment: Mainly cloud service

Commercial

Tableau

Licensing: Proprietary, subscription
Self-hosting: Yes — Tableau Server
Setup: Easy desktop authoring
Visualization: Highly polished, drag-and-drop
SQL: Custom SQL supported
Customization: Extensions and APIs
Ideal for: Visual analysts, large enterprises
Deployment: Cloud or managed server

Open source + paid

Metabase

Licensing: Open-source edition + commercial plans
Self-hosting: Yes — Docker or JAR
Setup: Simple
Visualization: Clean, fewer chart types
SQL: Native SQL editor + query builder
Customization: Moderate
Ideal for: Small teams, non-technical users
Deployment: Self-hosted or cloud

HONEST OVERVIEW

Apache Superset Advantages and Limitations

A balanced view to help you decide before you invest time in setup.

✓ Advantages

Free and open source with no per-seat licensing
Excellent SQL Lab for analysts who write SQL
Connects to a very wide range of SQL databases
Role-based permissions and embedding support
Self-hosted, so data access stays inside your infrastructure

! Limitations

Installation and upgrades need DevOps or Python skills
No official native Windows support
Production needs extra components (metadata DB, cache, workers)
Less guided than commercial tools for non-technical users
You are responsible for security patches, backups and scaling

USE CASES

Common Apache Superset Use Cases

Where Superset fits best in day-to-day analytics work.

Business Intelligence

Company-wide KPI dashboards on top of your data warehouse.

Marketing Analytics

Campaign, channel and funnel performance in one place.

Sales Reporting

Pipeline, revenue and regional sales tracking with filters.

Financial Dashboards

Budgets, costs and trends with controlled access by role.

Product Analytics

Feature adoption, retention and usage trends from event data.

Database Exploration

Ad-hoc SQL queries and quick charts for data teams.

Internal Reporting

Operational reports for support, logistics or HR teams.

Embedded Analytics

Embedding dashboards in internal tools via the embedding SDK.

TROUBLESHOOTING

Apache Superset Troubleshooting

Fixes for the problems people hit most often during setup.

Docker won’t start

An error such as services.superset-worker-beat.env_file.0 must be a string means your Compose version is outdated — upgrade and use docker compose (v2), not the old docker-compose. Make sure Docker has enough memory and disk available.

Blank or broken UI in dev mode

In the development compose setup, the superset-node container must finish building frontend assets before the UI renders. Setting NPM_RUN_PRUNE=true before docker compose up can clear npm-related issues.

Dependency or pip errors

Use a Python version supported by the release, work inside a fresh virtual environment, upgrade pip and setuptools, and install the OS build packages the docs list for your distribution.

Database connection fails

Install the right Python driver and check the SQLAlchemy URI. Inside Docker, localhost means the container itself — use a reachable hostname (for example host.docker.internal on Docker Desktop) instead.

Can’t log in

For Docker quickstart, the default is admin / admin. For pip installs, create a user with superset fab create-admin and run superset init after upgrades to refresh roles and permissions.

Browser can’t reach Superset

Confirm the service is listening on port 8088, that no other app is using it, and that firewalls or reverse proxies forward traffic correctly. Avoid network_mode: host in Compose — the docs note it breaks the setup.

FAQ

Apache Superset FAQs

Quick answers to the questions people search for most.