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
Efficient methods for loading data from Python using DataFrames, temporary files, or bulk inserts.
From Cloud Storage or over HTTPS
Load data into MotherDuck from S3, Azure, GCS, or public HTTPS URLs.
Load a DuckDB database into MotherDuck
Upload a local DuckDB database file to MotherDuck cloud storage.
From a PostgreSQL or MySQL Database
Learn to load a table from your PostgreSQL or MySQL database into MotherDuck.