Ibis provides a common dataframe-like interface to many popular databases and analytics tools (BigQuery, Snowflake, Spark, DuckDB, …). This lets users analyze data using the same consistent API, regardless of which backend they’re using, and without ever having to learn SQL. No more pains rewriting pandas code to something else when you run into performance issues; write your code once using Ibis and run it on any supported backend. In this tutorial users will get experience writing queries using Ibis on a number of local and remote database engines.

We love to use Python in our day jobs, but that enterprise database you run your ETL job against may have other ideas. It probably speaks SQL, because SQL is ubiquitous, it’s been around for a while, it’s standardized, and it’s concise.
But is it really standardized? And is it always concise? No!

Do we still need to use it? Probably!

What’s a data-person to do? String-templated SQL?
print(f”That way lies {{ m̴͕̰̻̏́ͅa̸̟̜͉͑d̵̨̫̑n̵̖̲̒͑̾e̸̘̼̭͌s̵͇̖̜̽s̸̢̲̖͗͌̏̊͜ }}”.)

Instead, come and learn about Ibis! It offers a dataframe-like interface to construct concise and composable queries and then executes them against a wide variety of backends (Postgres, DuckDB, Spark, Snowflake, BigQuery, you name it.).