Mike Bridge 6b2d75ca0b feat(activity-view): Phase 4 US2 — chart /activity/ endpoint (T028-T032)
Implements the chart cross-entity activity stream (US2). Builds on the
resolve_endpoint_path_entity helper from the prior tidy commit
(1d60513079), so the new endpoint is a small composition of: resolve
path entity → parse params → get_activity(Slice, ...) → serialize.

* T028 ChartRestApi.activity — @expose("/<uuid_str>/activity/") on the
  chart blueprint. Decorator stack mirrors list_versions; registered
  in include_route_methods. The MODEL_API_RW_METHOD_PERMISSION_MAP
  entry "activity": "write" added in Phase 3 already covers this
  endpoint family (the map is class-permission-agnostic).

* T029 TestChartActivityView class added to activity_view_tests.py,
  following the same patterns as TestDashboardActivityView.

* T030 test_chart_activity_includes_dataset_edit_as_related — Given
  a chart pointing at the birth_names dataset, When the dataset's
  description is edited, Then the chart activity stream surfaces a
  SqlaTable/related record (AS-1 of US2).

* T032 test_chart_activity_excludes_sibling_dashboards — Given the
  chart is on a dashboard, When the dashboard's title is edited,
  Then NO Dashboard records appear in the chart's activity. Per the
  spec's Relationship Traversal section: charts don't see sideways
  to dashboards they happen to be on.

* T031 (datasource-switch attribution) deferred — needs a second
  dataset fixture which the birth_names environment doesn't provide
  cleanly. Will land with a multi-dataset test harness in a follow-up.

Additional coverage: 404 for unknown UUID, 400 for malformed UUID,
400 for invalid include, 403 for non-owner, 200 envelope smoke,
chart-self-edit appears as source=self, include=self filter test.

Full suite: 19/19 integration tests + 57/57 unit tests green.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-28 15:37:25 -06:00
2024-04-15 11:21:42 -06:00

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

Repo Activity

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