jupysql VS Blazer

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.

I try to avoid these tools wherever possible, given the choice I'd always go for tools like Blazer.

https://github.com/ankane/blazer

No such luck in my current role, Looker and PowerBI are both in use by different bits of the org and nobody has the ability to delve into the underlying figures.

As u/jaxn said you could use Blazer for this kind of thing. I would also look into materialized views or custom tables and a scheduled job that calculates the metrics they care about. That will take you a long way. Eventually you can use something like Metabase but I would put that off for as long as possible as it's really expensive and pretty involved.

And it's Open Source: https://github.com/evidence-dev/evidence

I'd also highly recommend Blazer https://github.com/ankane/blazer if you are into the Ruby on Rails world. It's super solid, and it's been an indispensable tool integrated to all my projects.

I use Ahoy too, but I don't have very good visibility into the data. I should spend more time building queries and creating charts. I should probably set up blazer as well: https://github.com/ankane/blazer

blazer

The Blazer gem provides a nice way to analyze the results easily. It is simple to install and allows SQL queries to run against tables. The query here shows that the candidate implementation is significantly faster than the original.