Evan Rusackas e4cc16a9d7 ci: add Kesin11/actions-timeline to the heavy CI workflows
Adds a per-job/step Gantt chart (rendered as a mermaid diagram in the run
summary) to the 15 substantive CI workflows -- the 13 setup-backend
consumers plus superset-frontend.yml and docker.yml, the other two
heaviest CI paths. Skips trivial bot/label/notification workflows that run
in seconds and have nothing worth visualizing.

For single-job (or single-heavy-job-in-a-linear-chain) workflows, the step
is registered first, before checkout, so its post-processing hook -- which
is what actually renders the timeline -- captures the full job including
other steps' own cleanup. For workflows with multiple independent parallel
jobs, added a dedicated `actions-timeline` terminal job (`needs: [...]`,
`if: always()`) instead of duplicating the step into each parallel job:
the action fetches every job of the whole run from the GitHub API
regardless of which job it executes in, so one copy that runs after
every sibling job completes produces one authoritative timeline, while N
copies dropped into N parallel jobs would each race to render an
incomplete gantt before their siblings finish.

`expand-composite-actions: true` is set everywhere so setup-backend's
internal steps (Python setup, uv install, apt package caching, dependency
install) show up as their own bars rather than one opaque blob -- directly
useful given the last two PRs' worth of composite-action changes.

`actions: read` is added wherever needed to read job/step timing from the
Actions API, either to the workflow's top-level `permissions:` (when the
job in question has no job-level override) or directly into the relevant
job's own `permissions:` block (when one already exists, since a
job-level block replaces rather than merges with the workflow-level one).
2026-07-27 22:10:27 -07:00

Superset

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Superset logo (light)

A modern, enterprise-ready business intelligence web application.

Documentation

  • User Guide — For analysts and business users. Explore data, build charts, create dashboards, and connect databases.
  • Administrator Guide — Install, configure, and operate Superset. Covers security, scaling, and database drivers.
  • Developer Guide — Contribute to Superset or build on its REST API and extension framework.

Why Superset? | Supported Databases | Release Notes | Get Involved | Resources | Organizations Using Superset

Why Superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

Superset provides:

  • A no-code interface for building charts quickly
  • A powerful, web-based SQL Editor for advanced querying
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • Out of the box support for nearly any SQL database or data engine
  • A wide array of beautiful visualizations to showcase your data, ranging from simple bar charts to geospatial visualizations
  • Lightweight, configurable caching layer to help ease database load
  • Highly extensible security roles and authentication options
  • An API for programmatic customization
  • A cloud-native architecture designed from the ground up for scale

Screenshots & Gifs

Video Overview

superset-video-1080p.webm


Large Gallery of Visualizations


Craft Beautiful, Dynamic Dashboards


No-Code Chart Builder


Powerful SQL Editor


Supported Databases

Superset can query data from any SQL-speaking datastore or data engine (Presto, Trino, Athena, and more) that has a Python DB-API driver and a SQLAlchemy dialect.

Here are some of the major database solutions that are supported:

Amazon Athena   Amazon DynamoDB   Amazon Redshift   Apache Doris   Apache Drill   Apache Druid   Apache Hive   Apache Impala   Apache Kylin   Apache Pinot   Apache Solr   Apache Spark SQL   Ascend   Aurora MySQL (Data API)   Aurora PostgreSQL (Data API)   Azure Data Explorer   Azure Synapse   ClickHouse   Cloudflare D1   CockroachDB   Couchbase   CrateDB   Databend   Databricks   Denodo   Dremio   DuckDB   Elasticsearch   Exasol   Firebird   Firebolt   Google BigQuery   Google Sheets   Greenplum   Hologres   IBM Db2   IBM Netezza Performance Server   MariaDB   Microsoft SQL Server   MonetDB   MongoDB   MotherDuck   OceanBase   Oracle   Presto   RisingWave   SAP HANA   SAP Sybase   Shillelagh   SingleStore   Snowflake   SQLite   StarRocks   Superset meta database   TDengine   Teradata   TimescaleDB   Trino   Vertica   YDB   YugabyteDB

A more comprehensive list of supported databases along with the configuration instructions can be found here.

Want to add support for your datastore or data engine? Read more here about the technical requirements.

Installation and Configuration

Try out Superset's quickstart guide or learn about the options for production deployments.

Get Involved

Contributor Guide

Interested in contributing? Check out our Developer Guide to find resources around contributing along with a detailed guide on how to set up a development environment.

Resources

Understanding the Superset Points of View

Languages
Python 39.6%
TypeScript 38.2%
Jupyter Notebook 19.3%
HTML 2.3%
JavaScript 0.3%
Other 0.2%