Integration pages for connecting self-hosted Superset to the query engines and warehouses buyers most often pair it with, based on the database connection data in the Superset documentation.
Using Superset as the BI layer over a Trino query engine: driver and catalog connection setup, user impersonation so Trino sees the real user, SSH tunneling, query cancelation and performance trade-offs for federated queries.
Connecting Superset to ClickHouse for real-time dashboards: the recommended clickhouse-connect driver, connection string and port, driver version requirements across Superset releases, and functions Superset disallows by default.
Querying Apache Druid from Superset over Druid SQL with pydruid: connection string, TLS certificate options, join limitations and time grain support for event and time-series dashboards.
Running self-hosted Superset on top of Snowflake: installing snowflake-sqlalchemy into the image, role and warehouse in the connection string, credential handling and keeping warehouse cost under control with caching.
Connecting Superset to BigQuery with sqlalchemy-bigquery and a service account: credentials in Secure Extra, project-level connection strings, cost per query considerations and caching dashboards to limit scans.
Pointing Superset at Databricks SQL warehouses and clusters with the databricks extra: connector and dialect version requirements, token-based connection strings and catalog handling.
Connecting Superset to PostgreSQL as an analytics source: the bundled psycopg2 driver, connection string and SSL options, read-only roles, query performance on operational databases and keeping it separate from the metadata database.
Querying MySQL and MariaDB from Superset: the mysqlclient driver, connection string, TLS requirements, read replicas for dashboards and the limits of running analytics on a transactional database.
Running Superset on Redshift provisioned clusters or Redshift Serverless: the sqlalchemy-redshift driver, IAM authentication without passwords, cross-account role assumption and controlling query load from dashboards.
Querying data in S3 through Athena from Superset: the PyAthena driver, S3 staging directory, credentials, per-query scan cost and caching dashboards to keep Athena bills down.
Using StarRocks as a fast analytics engine behind Superset dashboards: the starrocks driver, catalog and database in the connection string, external catalogs over lakehouse tables and concurrency for many dashboard users.
Connecting Superset to Apache Doris for real-time dashboards: the pydoris driver, catalog-qualified connection strings, TLS verification of the frontend certificate and query concurrency.
Building user-facing and operational dashboards on Apache Pinot with Superset: the pinotdb driver, broker and controller URLs, query limits and pairing Pinot with Kafka ingestion.
Using DuckDB files or MotherDuck as a Superset data source: the duckdb-engine driver, file paths and in-memory databases, Parquet and CSV querying, concurrency limits of an in-process engine and MotherDuck tokens.
Connecting Superset to SQL Server and Azure Synapse: the pymssql driver, connection strings, Windows and Azure authentication limits, and reporting from SQL Server without slowing transactional workloads.
Reporting on Oracle Database from Superset: the python-oracledb driver that replaced cx_Oracle, connection strings and service names, read-only accounts and query limits.
Charting Elasticsearch indexes in Superset through the SQL interface: the elasticsearch-dbapi driver, connection string, what Elasticsearch SQL can and cannot do, and when a dedicated search UI fits better.
Connecting Superset to Hadoop-era and Spark engines: PyHive for Spark SQL and Hive through the Thrift server, impyla for Impala, latency expectations and moving dashboards to faster engines.
Reporting on SAP HANA with Superset: the hdbcli and sqlalchemy-hana drivers, connection strings and ports, read-only technical users and where Superset fits next to SAP reporting tools.
Querying Google Sheets as SQL tables in Superset through shillelagh: service account setup, sharing sheets with the service account email, the limits of impersonation and when to move a sheet into a database.
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