Mike Bridge 440e298c4f docs(activity-view): UPDATING.md note + T049 OpenAPI verification
Two doc-shaped tasks landed together because they're both about making
the activity-view endpoints discoverable by external consumers.

T047 — UPDATING.md: new section under the existing entity-version-history
entry documenting the three activity-view endpoints (dashboard / chart /
dataset), their query params (since / until / include / page / page_size),
the response shape with all DTO fields, the silent permission filter
(AV-008), tombstone behaviour (D-15), and the no-feature-flag / no-
new-tables impact statement. Mirrors the depth of the sc-103156
versioning section above it.

T048 satisfied by the UPDATING.md entry: the activity-view feature
adds no new config keys (no SUPERSET_* env vars, no feature flag), and
the per-endpoint API reference is auto-generated from the YAML
docstrings via FAB's OpenAPI integration. The `/swagger/v1` page picks
up the activity endpoints automatically — verified by the new tests
below. sc-103156 followed the same pattern (UPDATING.md only, no
standalone config doc).

T049 — Three new tests in TestActivityOpenApiSpec verify FAB's OpenAPI
generation includes the activity endpoints with the right shape:

* test_three_activity_paths_appear_in_openapi — the three
  /<uuid_str>/activity/ paths are surfaced in /api/v1/_openapi.
* test_activity_endpoints_document_query_params — since / until /
  include / page / page_size are all declared, and include's enum is
  exactly {"self", "related", "all"}.
* test_activity_endpoints_declare_200_response — 200 + 400/401/403/404
  are all declared response codes.

base_api_tests.py::TestOpenApiSpec::test_open_api_spec already
validates the full spec's structural correctness on every CI run, so
malformed YAML in the activity-view docstrings would have been caught
upstream. The new tests add activity-specific assertions about the
endpoints' presence and parameter shape.

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

Superset

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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

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Video Overview

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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

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