The DuckDB project has reached 1 million downloads per month, with users drawn to its ease of use, speed, and cost-effectiveness. Many "normie" users appreciate its Python wrapper, ability to be dropped into any Python script or notebook, and integration with other libraries. Local data workflows are gaining popularity, with users leveraging DuckDB to load data locally, query GCP or S3 buckets from a local machine, and create notebooks or data apps that showcase their work. The community is exploring ways to use DuckDB's columnar query engine for analytics, particularly for aggregation of event data and "one big table" architectures. Users often bring the database engine to their data, using AWS Lambda or Google Cloud Function to process data close to the engine, and some are experimenting with building cost-effective data engineering pipelines. Additionally, some users are dropping DuckDB into their BI tooling, such as Metabase, which supports on-demand embedded DuckDB for always-up file storage.