Superset Dev 936bd24ef7 feat(ci): expand testcontainers coverage to mssql, oracle, db2, elasticsearch
Adds four more dialects to the testcontainers suite (cockroachdb, crate,
trino from the initial pilot): mssql, oracle, db2, elasticsearch. All four
have native testcontainers-python container classes.

Restructures the workflow from one job running the whole suite to a
matrix, one job per dialect, running in parallel. A single slow container
would otherwise inflate wall-clock time for every dialect, not just its
own -- matrixing bounds total suite time by the slowest dialect instead of
the sum of all of them. Also renames the workflow file/name from
"Nightly-Testcontainers" to "Testcontainers" now that it runs on
pull_request (scoped via `paths`) in addition to the nightly cron.

Elasticsearch needed a different data-setup approach than the SQL-native
dialects: indices/documents get created via its REST API, not SQL INSERT,
matching how Superset actually encounters Elasticsearch in practice.
Confirmed empirically that Elasticsearch's SQL layer has no OFFSET support
at all (a real protocol limitation, already correctly documented via
ElasticSearchEngineSpec.supports_offset = False) and adjusted that
dialect's pagination test accordingly -- LIMIT/ORDER BY only, no OFFSET.

mssql and db2 could not be verified locally (no arm64 images for either;
this environment is Apple Silicon), same situation as crate's amd64-only
image from the initial pilot. Both are written against verified library
source (dialect names, connection URL construction) and will get their
first real execution on CI.
2026-09-05 22:24:51 -07:00

Superset

License Latest Release on Github Build Status PyPI version PyPI GitHub Stars Contributors Last Commit Open Issues Open PRs Get on Slack Documentation Storybook Bundle Analyzer

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 41.6%
TypeScript 37.9%
Jupyter Notebook 17.8%
HTML 2.1%
JavaScript 0.3%
Other 0.2%