Superset
by apache
Modern BI Platform for Interactive Data Exploration
Enterprise-grade business intelligence platform combining SQL exploration with intuitive dashboards for teams to visualize and analyze data at scale.
- 70,090+ GitHub stars
- Built with TypeScript
- Rich SQL IDE with syntax highlighting, query history, and result visualization
- Apache License 2.0 license
About This Project
Apache Superset transforms how organizations interact with their data by providing a comprehensive business intelligence solution that bridges the gap between technical and non-technical users. Built on a modern stack with React and Flask, it offers both a powerful SQL IDE for data engineers and drag-and-drop visualization tools for analysts.
The platform excels at connecting to virtually any SQL database, from traditional relational systems to modern data warehouses like Snowflake and BigQuery. Users can create interactive dashboards with dozens of visualization types, from basic charts to complex geospatial maps, all while maintaining enterprise-grade security with granular access controls and row-level permissions.
What sets Superset apart is its flexibility and extensibility. The semantic layer allows teams to define metrics once and reuse them across the organization, ensuring consistency. The caching layer delivers sub-second query performance even on massive datasets, while the plugin architecture enables custom visualizations tailored to specific business needs.
As an Apache Software Foundation project with nearly 70,000 GitHub stars, Superset benefits from active community development and enterprise adoption. It's cloud-native, supports multi-tenancy, and can be deployed anywhere from Kubernetes clusters to traditional servers, making it suitable for startups and Fortune 500 companies alike.
Key Features
- Rich SQL IDE with syntax highlighting, query history, and result visualization
- 50+ pre-built visualization types including charts, tables, maps, and custom plugins
- Semantic layer for defining reusable metrics and calculated columns
- Enterprise security with LDAP/OAuth integration and row-level permissions
- Advanced caching strategies for optimized query performance at scale
How You Can Use It
Building executive dashboards with real-time KPI monitoring and drill-down capabilities
Enabling self-service analytics where business users explore data without SQL knowledge
Creating embedded analytics experiences within existing applications via REST API
Performing ad-hoc data analysis with the integrated SQL Lab for complex queries
Developing data catalogs with semantic layers for consistent metric definitions across teams
Who Is This For?
Data engineers, business analysts, data scientists, and BI teams seeking an open-source alternative to Tableau or Looker with enterprise features