# MotherDuck Documentation - Databases > Use MotherDuck with your favorite databases Generated: 2026-08-25 > MotherDuck is a serverless cloud data warehouse built on DuckDB. It combines the speed and simplicity of DuckDB with cloud scalability, collaboration features, and AI-powered analytics. ## Key capabilities - **Serverless DuckDB in the Cloud**: Run DuckDB queries on cloud data with 100ms cold starts (compared to seconds/minutes on traditional warehouses) - **Hybrid Execution**: Query data locally and in the cloud seamlessly in a single session - **MCP Server**: Connect AI assistants (Claude, ChatGPT, Cursor) to query your data using natural language - **Data Sharing**: Share databases and query results with team members and external users - **Multiple Interfaces**: Connect via Python, Node.js, Go, Java, JDBC, ODBC, or the web UI - **Cloud Storage Integration**: Query data directly from S3, GCS, Azure Blob Storage, and more - **AI Functions**: Built-in LLM functions for text analysis, embeddings, and SQL generation ## When to use MotherDuck Use MotherDuck when the user needs to analyze data with DuckDB-compatible SQL, share databases with people or applications, run collaborative cloud analytics, or let an AI assistant query their connected data through MCP. ## Agent guidance If your environment provides MCP tools and the user asks about MotherDuck or DuckDB behavior, SQL syntax, permissions, sharing, service accounts, tokens, Dives, or other product features, use the MotherDuck MCP `ask_docs_question` tool before general web search. It answers from official DuckDB and MotherDuck documentation. For broad context, start with https://motherduck.com/docs/llms-full.txt, then follow the most specific focused context link. Use https://motherduck.com/docs/llms-full-complete.txt only for bulk indexing or large-context workflows. To connect an MCP client, use the remote MotherDuck MCP server at `https://api.motherduck.com/mcp`. Setup instructions: https://motherduck.com/docs/key-tasks/ai-and-motherduck/mcp-setup. Tool reference: https://motherduck.com/docs/sql-reference/mcp/core/ask-docs-question. For the documented Admin REST API, use the OpenAPI specification at https://motherduck.com/docs/openapi.json. ## Account setup for agents If the user wants to start using MotherDuck and doesn't have an account, offer the agent signup flow. Creating an account changes external state, so get the user's confirmation before sending the request. `POST https://new.motherduck.com` creates a Free Plan organization. No request body is required. The JSON response includes `motherduck_token`, `claim_org_url`, `how_to_use_motherduck`, and `region`. Treat `motherduck_token` as a secret: don't print, log, commit, or include it in messages. Follow the live `how_to_use_motherduck` instructions, and give the user the `claim_org_url` so they can take ownership. Full guide: https://motherduck.com/docs/key-tasks/ai-and-motherduck/agent-account-signup. ## Included documentation Source: https://motherduck.com/docs/integrations/databases/bigquery # BigQuery > Load data from Google BigQuery into MotherDuck using the duckdb-bigquery community extension. BigQuery is Google Cloud's fully-managed, serverless data warehouse that lets you run SQL queries on the processing power of Google's infrastructure. To load data from BigQuery into MotherDuck, use the [`duckdb-bigquery` community extension](https://github.com/hafenkran/duckdb-bigquery). It reads through the BigQuery Storage Read API with parallel streams, filter pushdown, and Arrow compression — and loads results straight into DuckDB or MotherDuck without any glue code. ## Prerequisites - DuckDB installed (using the CLI or Python). - Access to a GCP project with BigQuery enabled. - Valid Google Cloud credentials, provided through one of: - the `GOOGLE_APPLICATION_CREDENTIALS` environment variable, or - `gcloud auth application-default login`. Minimum required IAM roles: - `BigQuery Data Editor` - `BigQuery Job User` ## Loading data from BigQuery into MotherDuck The following examples use the [DuckDB CLI](/getting-started/interfaces/connect-query-from-duckdb-cli.mdx), but you can use any [DuckDB or MotherDuck client](/getting-started/interfaces/interfaces.mdx). ### Install and load the extension ```sql INSTALL bigquery FROM community; LOAD bigquery; ``` ### Attach a BigQuery project To read data from your project, attach it like you would attach a DuckDB database: ```sql ATTACH 'project=my-gcp-project' AS bq (TYPE bigquery, READ_ONLY); ``` To read from a public dataset, use the following syntax: ```sql ATTACH 'project=bigquery-public-data dataset=pypi billing_project=my-gcp-project' AS bq_public (TYPE bigquery, READ_ONLY); ``` ### Query a table Once attached, you can query BigQuery tables directly using standard SQL syntax: ```sql SELECT * FROM bq.dataset_name.table_name LIMIT 10; ``` Behind the scenes, this uses `bigquery_scan`. The extension also exposes two functions you can call directly: **`bigquery_scan`** — for direct reads from a single table: ```sql SELECT * FROM bigquery_scan('my_gcp_project.my_dataset.my_table'); ``` **`bigquery_query`** — for custom [GoogleSQL](https://cloud.google.com/bigquery/docs/introduction-sql), including views and external tables that the Storage Read API can't access on its own: ```sql SELECT * FROM bigquery_query( 'my_gcp_project', 'SELECT * FROM `my_gcp_project.my_dataset.my_table` WHERE column = "value"' ); ``` Both functions share the same Arrow scan engine. For very large reads, you can enable parallel read streams by relaxing DuckDB's default ordering guarantee: ```sql SET preserve_insertion_order = FALSE; ``` ### Load data into MotherDuck Verify the `motherduck_token` environment variable is set, then attach MotherDuck: ```sql ATTACH 'md:'; ``` Use `CREATE TABLE ... AS` to create a new table, or `INSERT INTO ... SELECT` to append data to an existing one: ```sql CREATE DATABASE IF NOT EXISTS pypi_playground; USE pypi_playground; CREATE TABLE IF NOT EXISTS duckdb_sample AS SELECT * FROM bq_public.pypi.file_downloads WHERE project = 'duckdb' AND timestamp = TIMESTAMP '2025-05-26 00:00:00' LIMIT 100; ``` --- Source: https://motherduck.com/docs/integrations/databases/postgres # PostgreSQL > Advanced open-source relational database with powerful features and extensibility. :::tip[Looking for a Postgres-compatible connection to MotherDuck?] Use the **[Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/)** to connect any Postgres-wire-compatible client — BI tools, ORMs, serverless runtimes, or languages without a DuckDB SDK — directly to MotherDuck. No extension required. ::: [PostgreSQL](https://www.postgresql.org) is an object-relational database management system (ORDBMS) based on POSTGRES, Version 4.2, developed at the University of California at Berkeley Computer Science Department. POSTGRES pioneered many concepts that only became available in some commercial database systems much later. As explained by DuckDB Lab's Hannes Mühleisen in the [explainer blog post](https://duckdb.org/2022/09/30/postgres-scanner.html): > PostgreSQL is designed for traditional transactional use cases, "OLTP", where rows in tables are created, updated and removed concurrently, and it excels at this. But this design decision makes PostgreSQL far less suitable for analytical use cases, "OLAP", where large chunks of tables are read to create summaries of the stored data. Yet there are many use cases where both transactional and analytical use cases are important, for example when trying to gain the latest business intelligence insights into transactional data. Choose the PostgreSQL workflow based on where your query needs to run. ## Query MotherDuck from PostgreSQL-compatible clients Use the [Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint) when an application, BI tool, or serverless runtime needs to connect to MotherDuck through the PostgreSQL wire protocol. This is the preferred path for PostgreSQL-compatible clients because it does not require installing or operating a PostgreSQL extension. ## Load PostgreSQL data into MotherDuck Use [DuckDB's PostgreSQL extension](/key-tasks/loading-data-into-motherduck/loading-data-from-postgres) when a DuckDB client needs to read from PostgreSQL and copy data into MotherDuck. This workflow is best for one-time loads, backfills, and controlled client-side movement between PostgreSQL, DuckDB, and MotherDuck. ## Run DuckDB from inside PostgreSQL Use [pg_duckdb](/concepts/pgduckdb) when queries need to run inside a PostgreSQL server with DuckDB or MotherDuck access. This is useful when PostgreSQL-local tables need to be joined with DuckDB or MotherDuck data from the PostgreSQL environment itself. --- Source: https://motherduck.com/docs/integrations/databases/planetscale # PlanetScale > PlanetScale offers hosted PostgreSQL and MySQL Vitess Databases. MotherDuck supports PlanetScale Postgres via the pg_duckdb extension, as well as the Postgres Connector. In our internal benchmarking, pg_duckdb offers 100x or greater query acceleration for analytical queries when compared to vanilla Postgres. ## Prerequisites Before connecting PlanetScale to MotherDuck, ensure you have: - A PlanetScale account with a Postgres database created - The `pg_duckdb` extension enabled in your PlanetScale database (see [PlanetScale extension documentation](https://planetscale.com/docs/postgres/extensions/pg_duckdb)) - A MotherDuck account and authentication token (get your token from the [MotherDuck dashboard](https://app.motherduck.com)) - Database connection credentials from your PlanetScale dashboard (host, port, username, password, database name) ## Connecting pg_duckdb to MotherDuck To run pg_duckdb, ensure you add it to your [extensions in PlanetScale](https://planetscale.com/docs/postgres/extensions/pg_duckdb). :::tip Review the configuration parameters before deploying the extension. Once deployed, you can connect to MotherDuck with the following SQL statements. ::: ```sql -- Grant necessary permissions to the PlanetScale superuser GRANT CREATE ON SCHEMA public to pscale_superuser; -- Create the pg_duckdb extension in your Postgres database CREATE EXTENSION pg_duckdb; -- Enable a MotherDuck connection with your authentication token CALL duckdb.enable_motherduck(); ``` To swap tokens, you can drop the MotherDuck connection and then re-add with: ```sql -- Remove the existing MotherDuck server connection DROP SERVER motherduck CASCADE; -- Re-enable MotherDuck with a new authentication token CALL duckdb.enable_motherduck(); ``` ### Using read replicas with PlanetScale :::info Pg_duckdb will automatically round-robin between your replicas when you use a read-only token. When switching between a read-write and a read-only token, you will want to snapshot your database and then force sync as part of the hand-off. ::: Switching from read-write to read-only is done with the following SQL statement in Postgres: ```sql -- Create a snapshot of your MotherDuck database to ensure consistency SELECT * FROM duckdb.raw_query('CREATE SNAPSHOT OF '); -- Drop the existing MotherDuck connection DROP SERVER motherduck CASCADE; -- Re-enable MotherDuck with your read-only token CALL duckdb.enable_motherduck(); -- Refresh the database to sync with the snapshot SELECT * FROM duckdb.raw_query('REFRESH DATABASE '); ``` ### Reading from MotherDuck :::info By default, data in [MotherDuck is mapped to Postgres in two different ways](https://github.com/duckdb/pg_duckdb/blob/main/docs/motherduck.md#schema-mapping). This is because MotherDuck is designed to hold many databases in its global catalog, while Postgres traditionally has a single database in its catalog. - For data in `my_db.main`, it is mapped directly to the `public` schema in the Postgres database. - For data in any other database & schema, it is mapped to `ddb$database$schema` in the Postgres database. ::: Once the catalog is in sync between MotherDuck and Postgres, the data can be queried directly from Postgres. If it is out of sync for any reason, it can be re-sync'd with the following SQL command: ```sql -- Terminate the pg_duckdb sync worker to force a re-sync SELECT * FROM pg_terminate_backend(( SELECT pid FROM pg_stat_activity WHERE backend_type = 'pg_duckdb sync worker' )); ``` #### Sample MotherDuck queries Once the catalog is synchronized to Postgres, we can query the data as if it was normal data in Postgres. ```sql -- Query data from a MotherDuck database and schema -- Note: Non-main schemas use the ddb$database$schema naming convention SELECT * FROM "ddb$sample_data$nyc".taxi ORDER BY tpep_dropoff_datetime DESC LIMIT 10; ``` You can also join with data in Postgres. ```sql -- Join MotherDuck data with local Postgres tables SELECT a.col1, b.col2 -- MotherDuck table from a non-main schema FROM "ddb$my_database$my_schema".my_table AS a -- Local Postgres table in the public schema LEFT JOIN public.another_table AS b on a.key = b.key ``` The DuckDB `iceberg_scan` function also works as well: ```sql -- Use DuckDB's iceberg_scan function to query Iceberg tables SELECT COUNT(*) FROM iceberg_scan('https://motherduck-demo.s3.amazonaws.com/iceberg/lineitem_iceberg', allow_moved_paths := true) ``` :::info Two special helper functions exist to run queries directly with DuckDB: - **`duckdb.query`**: Returns tabular data, use for SELECT queries - **`duckdb.raw_query`**: Returns void, use for DDL queries such as Snapshot Creation and Database Refresh. This function keeps the database in-sync when handing off between read and write nodes. ::: ```sql -- Use duckdb.query for SELECT queries that return tabular data -- This example lists all databases in MotherDuck SELECT * FROM duckdb.query('FROM md_databases()') ``` ```sql -- Use duckdb.raw_query for DDL queries that return void -- This example drops a table in MotherDuck SELECT * FROM duckdb.raw_query('DROP TABLE my_database.my_schema.some_table') ``` ### Replicating data to MotherDuck :::tip For smaller tables, data can be replicated using simple SQL statements. ::: ```sql -- Create a table in MotherDuck and populate it with data from Postgres -- Replace my_database and my_schema with your target database and schema names CREATE TABLE "ddb$my_database$my_schema".my_table USING duckdb AS SELECT * FROM public.my_table ``` :::tip For larger tables, state management, and tighter SLAs & requirements, MotherDuck offers [integrations to various other ingestion partners](/integrations/ingestion/). ::: ### Further reading The [pg_duckdb github repo](https://github.com/duckdb/pg_duckdb) contains [further documentation](https://github.com/duckdb/pg_duckdb/blob/main/docs/README.md) of all available functions. For ease of finding the documentation, a table of the documentation sections is below: | Topic | Description | |-------|-------------| | [**Functions**](https://github.com/duckdb/pg_duckdb/blob/main/docs/functions.md) | Complete reference for all available functions | | [**Syntax Guide & Gotchas**](https://github.com/duckdb/pg_duckdb/blob/main/docs/gotchas_and_syntax.md) | Quick reference for common SQL patterns and things to know | | [**Types**](https://github.com/duckdb/pg_duckdb/blob/main/docs/types.md) | Supported data types and type mappings | | [**Extensions**](https://github.com/duckdb/pg_duckdb/blob/main/docs/extensions.md) | DuckDB extension installation and usage | | [**Settings**](https://github.com/duckdb/pg_duckdb/blob/main/docs/settings.md) | Configuration options and parameters | | [**Transactions**](https://github.com/duckdb/pg_duckdb/blob/main/docs/transactions.md) | Transaction behavior and limitations | ## Connecting with the Postgres extension You can also connect to PlanetScale Postgres with the DuckDB Postgres extension. This approach lets you query PlanetScale data directly from DuckDB or MotherDuck. ### Install and load the extension ```sql -- Install the Postgres extension from DuckDB's extension registry INSTALL postgres; -- Load the extension to enable Postgres connectivity LOAD postgres; -- Attach your PlanetScale database using a connection string ATTACH '' AS postgres_db (TYPE postgres); ``` ### Connection string format The connection string format follows PostgreSQL's standard connection parameters. Here's an example with explanations: ```sql ATTACH 'host= port= user= password= dbname= sslmode=require' AS planetscale (TYPE postgres); ``` **Connection Parameters:** - `host`: Your PlanetScale database hostname (found in your PlanetScale dashboard) - `port`: The database port (typically 3306 for MySQL or 5432 for Postgres) - `user`: Your PlanetScale database username - `password`: Your PlanetScale database password - `dbname`: The name of your database in PlanetScale - `sslmode=require`: Ensures SSL encryption is used (required for PlanetScale) :::info The above connection string works with DuckDB. PlanetScale suggests also using the `sslnegotiation` and `sslrootcert` keys when connecting to Postgres, but these keys are not supported by the `libpq` version that is included in DuckDB. The `sslmode=require` parameter is sufficient for secure connections. ::: --- Source: https://motherduck.com/docs/integrations/databases/sql-server # SQL Server > Use the SQL Server replication guide when you need to read tables or queries from SQL Server and write the results to MotherDuck. The guide covers Python, pyodbc, SQL Server authentication, and loading dataframe results into MotherDuck. ## How it works with MotherDuck 1. Connect to SQL Server with the Microsoft ODBC driver and `pyodbc`. 2. Read a SQL Server table or query result into a dataframe. 3. Connect to MotherDuck from Python and persist the dataframe as a MotherDuck table. ## Related content - [Replicating SQL Server tables to MotherDuck](/key-tasks/data-warehousing/replication/sql-server) - [Loading data into MotherDuck](/key-tasks/loading-data-into-motherduck/) - [MotherDuck authentication](/key-tasks/authenticating-and-connecting-to-motherduck/authenticating-to-motherduck) --- Source: https://motherduck.com/docs/integrations/databases/index # Databases > Use MotherDuck with your favorite databases MotherDuck integrates directly with popular databases to help you build data pipelines and applications. ## Included pages - [BigQuery](https://motherduck.com/docs/integrations/databases/bigquery): Load data from Google BigQuery into MotherDuck using the duckdb-bigquery community extension. - [PostgreSQL](https://motherduck.com/docs/integrations/databases/postgres): Advanced open-source relational database with powerful features and extensibility. - [PlanetScale](https://motherduck.com/docs/integrations/databases/planetscale): PlanetScale offers hosted PostgreSQL and MySQL Vitess Databases. MotherDuck supports PlanetScale Postgres via the pg_duckdb extension, as well as the Postgres Connector. In our internal benchmarking, pg_duckdb offers 100x or greater query acceleration for analytical queries when compared to vanilla Postgres. - [SQL Server](https://motherduck.com/docs/integrations/databases/sql-server): Use the SQL Server replication guide when you need to read tables or queries from SQL Server and write the results to MotherDuck. The guide covers Python, pyodbc, SQL Server authentication, and loading dataframe results into MotherDuck. - [MySQL](https://motherduck.com/docs/integrations/databases/mysql): MySQL is a relational database commonly used for application data. DuckDB's MySQL extension can read from MySQL-compatible databases, which lets you copy selected data into MotherDuck from a DuckDB client. - [Supabase](https://motherduck.com/docs/integrations/databases/supabase): Supabase is a Postgres platform for building applications with a managed database, APIs, authentication, storage, and realtime features. Supabase's documented DuckDB Wrapper can query MotherDuck from a Supabase Postgres database through a foreign data wrapper. --- Source: https://motherduck.com/docs/integrations/databases/mysql # MySQL > MySQL is a relational database commonly used for application data. DuckDB's MySQL extension can read from MySQL-compatible databases, which lets you copy selected data into MotherDuck from a DuckDB client. ## How it works with MotherDuck 1. Connect to MotherDuck from the DuckDB CLI, Python, or another DuckDB client. 2. Install and load DuckDB's MySQL extension in that session. 3. Attach the MySQL database, then create MotherDuck tables from selected MySQL tables or queries. ## Example ```sql INSTALL mysql; LOAD mysql; ATTACH 'host=localhost port=3306 user=my_user password=my_password database=my_database' AS mysql_db (TYPE mysql); CREATE TABLE my_table AS SELECT * FROM mysql_db.my_schema.my_table; ``` ## Related content - [DuckDB MySQL extension documentation](https://duckdb.org/docs/current/core_extensions/mysql.html) - [Loading data from PostgreSQL-compatible sources](/key-tasks/loading-data-into-motherduck/loading-data-from-postgres) - [Running hybrid queries](/key-tasks/running-hybrid-queries) --- Source: https://motherduck.com/docs/integrations/databases/supabase # Supabase > Supabase is a Postgres platform for building applications with a managed database, APIs, authentication, storage, and realtime features. Supabase's documented DuckDB Wrapper can query MotherDuck from a Supabase Postgres database through a foreign data wrapper. ## How it works with MotherDuck 1. Enable the Supabase Wrappers extension. 2. Create the `duckdb_wrapper` foreign data wrapper. 3. Store a MotherDuck token in Supabase Vault, then create a foreign server with `type 'md'`, the MotherDuck database name, and the Vault-backed token option. 4. Create a schema for the foreign tables. 5. Import a MotherDuck schema, such as `main`, into Supabase and query the imported foreign tables from Postgres. ```sql create extension if not exists wrappers with schema extensions; create foreign data wrapper duckdb_wrapper handler duckdb_fdw_handler validator duckdb_fdw_validator; create server duckdb_server_md foreign data wrapper duckdb_wrapper options ( type 'md', database 'my_db', vault_motherduck_token '' ); create schema if not exists duckdb; import foreign schema "main" from server duckdb_server_md into duckdb; select * from duckdb.my_table limit 10; ``` The Supabase DuckDB Wrapper is a read path into MotherDuck: it supports querying foreign tables, including `where`, `order by`, and `limit` pushdown, but does not support inserts, updates, deletes, or truncates through the foreign tables. ## Related content - [View the full process in the Supabase DuckDB Wrapper documentation](https://supabase.com/docs/guides/database/extensions/wrappers/duckdb) - [MotherDuck authentication](/key-tasks/authenticating-and-connecting-to-motherduck/authenticating-to-motherduck) - [PostgreSQL and MotherDuck](/integrations/databases/postgres) --- ## Docs feedback MotherDuck accepts optional user-submitted feedback about this page at `GET https://motherduck.com/docs/api/feedback/agent`. 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