Loading Data into MotherDuck
You can leverage MotherDuck's managed storage to persist your data. MotherDuck storage provides a high level of manageability and abstraction, optimizing your data for secure, durable, performant, and efficient use. There are several ways to load data into MotherDuck storage.
Before You Start: Understanding Trade-offs
Before choosing a loading method, it's important to understand the performance implications and trade-offs involved. Our Considerations for Loading Data guide explains:
- Batch vs. streaming approaches and when to use each
- File format choices and their impact on performance
- Optimal batch sizes for different scenarios
- Cost implications of different loading strategies
- Common performance pitfalls and how to avoid them
This understanding will help you make informed decisions that optimize for your specific use case.
Loading Data Best Practices
Understanding trade-offs and performance implications when loading data into MotherDuck
From Your Local Machine
Moving data from local to MotherDuck through the UI or programmatically.
Loading data to MotherDuck with Python
As you ingest data using Python, typically coming from API or other sources, you have different options to load data to MotherDuck.
From Cloud Storage or over HTTPS
MotherDuck supports several cloud storage providers, including Amazon S3, Azure, Google Cloud and Cloudflare R2.
Load a DuckDB database into MotherDuck
MotherDuck supports uploading local DuckDB databases in the cloud as referenced by the CREATE DATABASE statement.
From a PostgreSQL or MySQL Database
Learn to load a table from your PostgreSQL or MySQL database into MotherDuck.