pg_cron VS Pandas

[Postgres Extensions] pg_cron 1.5.2 (new projects only) now supports sub-minute schedules. PR

(Your PostgreSQL installation might already have pg_cron available, but if not, you would need to install it)

I’d set up citusdata/pg_cron in 14, so I’ll need to set that up in 15 as well.

Functions can be executed via a database query or on a regular schedule. Trigger functions are a special type of function that are covered in a previous article. They provide a way to execute an action based on a database event.

* extraction and loading: the psql-http PostgreSQL extension which is available in Supabase (but not all services like AWS RDS). Not sure how it works with OAuth but something to look into. * automation: use pg_cron to automate the ingest. * transformation: leverage Postgresql JSON functions.

A low-level alternative is using a PostgreSQL-side facility like https://github.com/citusdata/pg_cron - this, however, requires installing a PostgreSQL extension.

If you just want scheduling queries, you can use this https://github.com/citusdata/pg_cron

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.