Claude Code 90e86ae3a4 fix(dashboard-export): include and re-attach roles in import/export
Dashboards restricted via `DASHBOARD_RBAC` had their role assignments
silently dropped from export bundles, so a round-trip recreated the
dashboard with no access restriction — a 'least privilege' dashboard
became 'all roles can access' on import. `Dashboard.export_fields`
doesn't include `roles` (it's a many-to-many relationship, not a
column), so `export_to_dict` skipped it entirely.

This change:

- **Export** (`export.py:_file_content`): emit `roles` as a list of
  role *names* in the YAML when the dashboard has any. Names rather
  than IDs because IDs are environment-local — names are the only
  thing that can cross environments. Omits the key entirely when
  there are no role restrictions (older import code treats "missing"
  as "no restriction"; an empty list could confuse importers that
  distinguish the two states).

- **Import** (`importers/v1/utils.py:import_dashboard`): pop `roles`
  before handing config to `import_from_dict` (the standard SQLAlchemy
  path doesn't resolve role names into role objects), then resolve each
  name via `security_manager.find_role` and assign `dashboard.roles`
  after creation. Roles that don't exist in the destination are skipped
  with a warning rather than failing the whole import.

Companion regression tests (added in this PR's earlier commit) cover
both the populated-roles and no-roles cases on the export side.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-05-15 01:15:24 -07:00
2025-12-04 13:18:34 -05:00

Superset

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A modern, enterprise-ready business intelligence web application.

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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
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  • A lightweight semantic layer for quickly defining custom dimensions and metrics
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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.

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

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