This book covers DuckDB—a modern, fast, embedded analytical database. It runs on your machine and can easily process many gigabytes of data from a variety of sources, including JSON, CSV, Parquet, SQLite, and Postgres. DuckDB integrates well into the Python and R ecosystems and allows you to query in-memory data frames without copying the data. You don’t need to spin up cloud data warehouses for your day-to-day data processing anymore; you can just run DuckDB on your data, locally or in the cloud.

What is DuckDB?
DuckDB is a modern embedded analytics database that runs on your machine and lets you efficiently process and query gigabytes of data from different sources. Embedded databases run within another process, like your application or notebook, and are not accessed over a network. DuckDB was created in 2018 by Mark Raasveldt and Hannes Miihleisen, who, at the time, were researchers in database systems at Centrum Wiskunde & Informatica (CWI)—the national research institute for mathematics and computer science in the Netherlands.
The founders and the CWI spun DuckDB Labs off as a startup to further develop DuckDB. Its engineering team focuses on making DuckDB more efficient, user friendly, and better integrated.
The nonprofit DuckDB Foundation governs the DuckDB Project by safeguarding the intellectual property and ensuring the continuity of the open source project under the MIT license. The foundation’s operations and DuckDB’s development are supported by commercial members, while association members can inform the development road map.
Contents.
1 An introduction to DuckDB.
2 Getting started with DuckDB.
3 Executing SQL queries.
4 Advanced aggregation and analysis of data.
5 Exploring data without persistence.
6 Integrating with the Python ecosystem.
7 DuckDB in the cloud with MotherDuck.
8 Building data pipelines with DuckDB.
9 Building and deploying data apps.
10 Performance considerations for large datasets.
11 Conclusion.
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