The author shares a shift from using Excel and Google Sheets to DuckDB and SQL for handling CSV files. Specifically, they highlight how a simple task of extracting unique user IDs from a CSV has become more efficient with SQL commands. Instead of relying on Excel's pivot tables, they now execute a straightforward SQL query: `select distinct(user_id) from './export.csv';`. This change not only simplifies the extraction process but also allows for immediate export of the results to another CSV file. The conversation in the comments reveals a variety of experiences with DuckDB. Some users appreciate its ability to perform SQL operations, pointing out features like applying joins and window functions directly on CSV files. Others mention using specific commands, such as `SUMMARIZE './export.csv';`, to quickly understand the data structure. There’s also a discussion about the limitations of Excel for complex data tasks, with users preferring SQL for its straightforwardness. Several participants reference tools like Microsoft’s LogParser and open-source alternatives like PondPilot.io that complement or compete with DuckDB. Users express excitement about the integration of SQL with various data sources, including direct reads from URLs or cloud storage. The overall sentiment is clear: more users are finding DuckDB to be a powerful and convenient alternative to traditional spreadsheet tools.