Load Data into MotherDuck

Source Of Truth

• Prefer current MotherDuck loading, cloud-storage, and Postgres-endpoint loading docs first.
• Use CREATE SECRET and cloud-storage docs for protected-object-store workflows.
• Use the DuckDB database upload docs when the source is an existing local .duckdb, .ddb, or attached DuckDB database.
• Keep the loading advice aligned with MotherDuck's documented posture:
  • batch over streaming
  • Parquet over CSV when you control the format
  • dataframe, COPY, CTAS, or INSERT ... SELECT over row-by-row inserts
  • native MotherDuck storage first unless DuckLake is explicitly required

Default Posture

• Start by classifying the source: object storage or HTTPS, local file or local DuckDB, in-memory rows, or an external database.
• Prefer CREATE TABLE AS SELECT for first loads and INSERT INTO ... SELECT for appends.
• For whole DuckDB databases, use CREATE OR REPLACE DATABASE remote_name FROM CURRENT_DATABASE(), an attached local database, a local file path from a native client, or a remote .duckdb file URL such as S3. Remote and local file imports physically copy data into MotherDuck; database/share clone sources are zero-copy.
• Use Parquet for durable bulk movement whenever you control the source format.
• Treat the Postgres endpoint as a thin-client path for server-side remote reads, not for local-file or extension-driven ingestion.
• Bootstrap the target MotherDuck database first when the ingestion tool does not create it automatically.
• Keep raw landing minimally transformed; do typing, deduplication, and business logic in staging or modeling steps.
• Keep source storage close to the MotherDuck region when you control placement.

Workflow

1. Identify where the source data actually lives.
2. Choose the loading path:
   • object storage or HTTPS: remote read into MotherDuck
   • local file or local DuckDB: use a DuckDB client path
   • remote DuckDB database file: use CREATE DATABASE ... FROM '<cloud-url>' with the required cloud secret
   • in-memory rows: Arrow or dataframe bulk load first, batched inserts only as a fallback
   • external database: use the appropriate scan or replication path from a DuckDB-capable environment
3. Land the data into a raw or staging table with minimal transformation.
4. Validate row counts, types, and a few business aggregates immediately after the load.
5. Promote into modeled tables only when the request includes transformation; a load request is complete after its destination data is validated.

For answer, review, or planning requests, recommend the loading path without mutating data. For load or implementation requests, perform the requested in-scope write and validation; ask before destructive replacement or a broader external write.

References

Read only the reference sections needed for the current task.

• Read references/INGESTION_PATTERNS.md for format-specific options, cloud-storage secrets, Postgres-endpoint loading tradeoffs, Python dataframe paths, and advanced ingestion patterns.

Related Skills

Load related skills only for missing capabilities; reuse established context.

• motherduck-connect for choosing between the Postgres endpoint and a DuckDB client path
• motherduck-explore for inspecting destination databases and validating landed tables
• motherduck-query for writing CTAS, append, and validation SQL
• motherduck-model-data for promoting landed data into staging and analytics tables
• motherduck-ducklake only when object-storage-backed lakehouse storage is an explicit requirement
• motherduck-cli when a shell-based load should stream structured output to files