11 Conclusion
Thank you for accompanying us on our journey through DuckDB in Action. We hope you have learned a lot about DuckDB and how it can be used to make your day-to-day data engineering life more productive and enjoyable. We’re glad we could share our excitement and passion for the power and usefulness of DuckDB and empower you to use it to solve your data engineering problems and to build amazing data products. In this chapter, we will summarize what we have learned, mention the areas we did not cover, and discuss the future of data engineering with DuckDB.
11.1 What we have learned in this book
We looked at how to get started with DuckDB, how to install it, and how to use the CLI and the Python API. Next, we illustrated how easy it is to load data from CSV, JSON, and Parquet files and then analyze it with DuckDB using SQL—even without creating tables for the data. We also explored how to use DuckDB via the Python APIs, both for SQL and fluent queries, and how tightly and efficiently it integrates with pandas DataFrames.
We learned how to make the most out of DuckDB with SQL, from the basics to more advanced features like window functions and CTEs. As part of our SQL explorations, we highlighted all the goodies that DuckDB adds to the standard SQL language, like support for JSON, nested data structures, advanced joins, and flexible selection, grouping, and aggregation.