[1] It is designed to provide high performance on complex queries against large databases in embedded configuration,[2] such as combining tables with hundreds of columns and billions of rows.

Unlike other embedded databases (for example, SQLite) DuckDB is not focusing on transactional (OLTP) applications and instead is specialized for online analytical processing (OLAP) workloads.

[11] DuckDB also deviates from the traditional client–server model by running inside a host process (it has bindings, for example, for a Python interpreter with the ability to directly place data into NumPy arrays[2]).

[12] [13] DuckDB uses a single-file storage format to store data on disk, designed to support efficient scans and bulk updates, appends and deletes.

While using SQL for queries, DuckDB targets serverless applications and provides extremely fast responses using Apache Parquet files for storage.

[8] The company has chosen not to take venture capital funding, stating "We feel investment would force the project direction towards monetization, and we would much prefer keeping DuckDB open and available for as many people as possible".