Modern, open-source data exploration and visualization at petabyte scale.
Apache Superset is a leading enterprise-grade data exploration and visualization platform designed to be fast, lightweight, and intuitive. Originally developed at Airbnb and now a Top-Level Project at the Apache Software Foundation, it provides a rich set of features for users of all skill levels to consume and interact with data. Architecturally, it is built on Python (Flask), React, and SQLAlchemy, allowing it to interface with virtually any SQL-based data source via a massive array of database drivers. In the 2026 market, Superset distinguishes itself by offering a cloud-native, highly extensible alternative to proprietary BI tools like Tableau and PowerBI, specifically excelling in environments requiring deep integration with modern data stacks (Snowflake, Databricks, ClickHouse). Its core 'SQL Lab' provides a powerful IDE for data engineers, while the 'no-code' viz builder empowers business users. The 2026 ecosystem emphasizes AI-assisted query generation and automated dashboard optimization, maintaining its position as the go-to open-source solution for high-growth tech companies and enterprise data teams needing granular control over their analytics infrastructure.
A full-featured SQL editor with metadata browsing, multi-statement execution, and query history persistence.
The open-source BI platform that turns your dbt project into a governed, version-controlled analytics engine.
Transform raw data into real-time metrics with a powerful semantic layer and automated BI dashboards.
The AI-powered data scientist that automates complex analysis, visualization, and predictive modeling through sandboxed code execution.
The world's most adaptable EPM platform for autonomous financial and operational planning.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Allows users to define custom dimensions and metrics (virtual columns) at the dataset level using SQL expressions.
Asynchronous query execution and results caching using Celery and Redis to handle high concurrency.
Granular security filters that append WHERE clauses to queries based on the user's role/attributes.
Extensible architecture using the Superset-UI framework to build and register custom React visualizations.
Support for Jinja macros in SQL Lab, allowing for dynamic queries based on dashboard filters or user roles.
Threshold-based alerting and scheduled dashboard delivery via Slack or Email using Chromium-based screen capture.
Engineers need to visualize high-velocity log data from ClickHouse without performance lag.
Registry Updated:2/7/2026
Deploy to NOC screens.
A SaaS provider wants to offer white-labeled dashboards to 1,000+ customers within their own portal.
Finance teams require complex calculated fields (EBITDA, Yo Y growth) across disparate tables.