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.
📄️ Considerations for Loading Data
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 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.