Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
Data Science
Towards Data Science·25d ago·5 min read
Building a Data Lakehouse with DuckDB and DuckLake
TL;DR
DuckDB and its DuckLake extension enable the creation of a cost-effective data lakehouse, allowing organizations to store and query large volumes of diverse data without the high costs associated with traditional solutions.
✦ Why It Matters
Start using DuckDB and DuckLake today to build a cost-effective data lakehouse for your organization.
Key Takeaways
- Install DuckDB and DuckLake to manage data lakes effectively.
- Utilize SQL for querying local and remote Parquet files.
- Leverage snapshots for time-travel queries and data recovery.
- Evolve table schemas without rewriting original data files.
How It Works
DuckLake manages metadata in a relational database rather than in co-located files, allowing for efficient tracking of data changes and schema evolution. This approach enables users to perform SQL operations on Parquet files while maintaining a transactional model.