jupysql VS prism

Hey, HN community!

We're stoked to launch JupySQL today! JupySQL is an open-source library that brings a modern SQL experience to Jupyter. JupySQL is compatible with all major databases, such as Snowflake, Redshift, PostgreSQL, MySQL, MariaDB, DuckDB, SQL Server, Clickhouse, Trino, and more!

To get started, check out our tutorial: https://jupysql.ploomber.io/en/latest/quick-start.html

SQL is the defacto language for data analysis; however, analysis often requires a mix of SQL and Python. JupySQL bridges this gap, allowing users to execute SQL queries seamlessly in Jupyter and continue their analysis in Python. Add %%sql to the top of your cell and start writing SQL.

Here are some of JupySQL's main features:

- Syntax highlighting

Please show your support with a 🌟: https://github.com/ploomber/jupysql

If anyone is looking for something like this in Python/Jupyter, check out JupySQL: https://github.com/ploomber/jupysql

How are people managing the existence of data frame APIs like pandas/polars with SQL engines like BigQuery, Snowflake, and DuckDB?

Most of my notebooks are a mix of SQL and Python: SQL for most processing, dump the results as a pandas dataframe (via https://github.com/ploomber/jupysql) and then use Python for operations that are difficult to express with SQL (or that I don't know how to do it), so I end up with 80% SQL, 20% Python.

Unsure if this is the best workflow but it's the most efficient one I've come up with.

Disclaimer: my team develops JupySQL.

🔗 Website: https://runprism.com/

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