Writing production-ready ETL pipelines in Python / Pandas

This course will show each step to write an ETL pipeline in Python from scratch to production using the necessary tools such as Python 3.9, Jupyter Notebook, Git and Github, Visual Studio Code, Docker and Docker Hub and the Python packages Pandas, boto3, pyyaml, awscli, jupyter, pylint, moto, coverage and the memory–profiler.

Two different approaches how to code in the Data Engineering field will be introduced and applied – functional and object oriented programming.

Best practices in developing Python code will be introduced and applied:

design principles

clean coding

virtual environments

project/folder setup

configuration

logging

exeption handling

linting

dependency management

performance tuning with profiling

unit testing

integration testing

dockerization

What is the goal of this course?

In the course we are going to use the Xetra dataset. Xetra stands for Exchange Electronic Trading and it is the trading platform of the Deutsche Borse Group. This dataset is derived near–time on a minute–by–minute basis from Deutsche Borse’s trading system and saved in an AWS S3 bucket available to the public for free.

The ETL Pipeline we are going to create will extract the Xetra dataset from the AWS S3 source bucket on a scheduled basis, create a report using transformations and load the transformed data to another AWS S3 target bucket.