The fourth tutorial in the Postgres related articles is an improvement on the Flask API I made last time. I will be using FastAPI to create the same API, but with the batteries included.

As a third tutorial in my Postgres related articles I am creating a basic REST API to retrieve data through Flask and SQLAlchemy from the Postgres database.

To continue the experiment with PostgreSQL I made a simple crawler that retrieves data with Scrapy and stores the data in the PostgreSQL database.

This tutorial describes how to set up PostgreSQL on Ubuntu and configure it to make it connect with Python.

A quick example on how to use xpaths to find values in a website based on certain elements. TLDR: "//tr[th//text()[contains(., 'Cost(s)')]]/td" will find the td where the th of the same row contains the text Costs(s).

This is a simple example on how to retrieve the changelog of your Jira tickets using Python and requests and calculate the lead time.

In this article I will upload a static website to IPFS to get myself familiar with the steps it takes to link an .eth domain to content hosted on the distributed web.

An exercise where I use Spark to forward fill a sparse dataset.

In this project I will crawl data from a website, upload it to MongoDB and make it accessible with GraphQL, all in Python!

Another connection with PySpark that I needed for one of the projects at work. For our daily sync with Salesforce we use Python with simple-salesforce which makes it easy to pull data, but for Spark it takes a little more effort to get data out.