dlt
INGESTION
dlt is an open-source data loading tool that simplifies the process of moving data from various sources into data warehouses or lakes. It supports a wide range of data sources and destinations, offering seamless ETL (Extract, Transform, Load) capabilities with a focus on usability and flexibility. dltHub allows users to manage data pipelines efficiently, ensuring data integrity and consistency throughout the process.
dlt + MotherDuck
dlt integrates with MotherDuck by enabling users to load data directly into the MotherDuck cloud data warehouse. This integration leverages dltHub's flexible ETL capabilities to move data from multiple sources into MotherDuck, allowing for efficient data management and analysis within the MotherDuck environment.
Customer Success Stories
Teams build lightweight data pipelines with dlt and MotherDuck:
- Reflex implemented their entire data pipeline in about 15 lines of code using dlt, demonstrating the simplicity of loading data into MotherDuck.
FAQS
What is dlt and how does it work with MotherDuck?
dlt (data load tool) is an open-source Python library for creating data pipelines. It has native DuckDB/MotherDuck support as a destination, letting you extract data from APIs, databases, and files, then load it directly into MotherDuck with automatic schema inference and evolution.
How do I configure dlt to load data into MotherDuck?
Install dlt with the DuckDB extra (pip install dlt[duckdb]), then configure your pipeline with MotherDuck as the destination using the connection string format md:your_database?motherduck_token=YOUR_TOKEN. dlt handles schema creation, data type mapping, and incremental loading automatically.
What sources can dlt load into MotherDuck?
dlt supports 100+ verified sources including REST APIs, databases (PostgreSQL, MySQL, MongoDB), cloud services (Salesforce, Stripe, GitHub), and file formats. You can also create custom sources with Python. All data flows through dlt's normalization layer before loading into MotherDuck.
Does dlt support incremental loading to MotherDuck?
Yes, dlt supports incremental loading with merge and append strategies. It tracks state between runs, so you only load new or changed data. This is efficient for large datasets and reduces both processing time and MotherDuck compute costs.
