Apache Superset is a modern, enterprise-ready business intelligence web application for data exploration and visualization. It supports a wide array of databases via SQLAlchemy, offering a no-code chart builder, a powerful SQL editor, and dynamic dashboards. The library is actively maintained, with major releases following a time-based schedule to provide predictable updates and supporting the latest minor versions of the last two major releases.
pip install apache-supersetNo compatibility data collected yet for this library.
This quickstart outlines the steps to install and run Apache Superset locally using pip and its CLI commands. Note that for a full-featured sandbox environment, especially for development, the official documentation often recommends using Docker Compose.
Migrate your authentication configuration from OpenID to OAuth, LDAP, or database authentication methods.
Always generate and configure a strong, random `SECRET_KEY` in `superset_config.py`. Superset versions 2.1.0 and later prevent startup with the default key.
Apply security updates and patches once the Apache Superset team releases a fix addressing this RLS bypass vulnerability.
Optimize SQL queries (select only needed columns, simplify joins), implement an efficient caching layer (e.g., Redis), and evaluate database performance for analytical workloads.
Install PostgreSQL development libraries on your system (e.g., `sudo apt-get install libpq-dev` on Debian/Ubuntu, `sudo yum install postgresql-devel` on CentOS/RHEL, or ensure PostgreSQL is installed and its `bin` directory is in `PATH` on macOS).
Before running Superset CLI commands, set environment variables: `export FLASK_APP=superset` and `export SUPERSET_CONFIG_PATH=/path/to/your/superset_config.py`.
Update your Docker Compose to a recent version. If using `docker-compose` command, consider migrating to `docker compose` (without the hyphen) as it's the newer CLI for Docker Compose V2.
Check the official Superset documentation for specific database connection requirements and known limitations. You may need to create a custom SQLAlchemy dialect or view to abstract away unsupported features for that specific database.