One of the main obstacles of Data Engineering is the large and varied technical skills that can be required on a day-to-day basis.
This aim of this repository is to help you develop and learn those skills. Generally, here are the high level topics that these practice problems will cover.
- Python data processing.
- csv, flat-file, parquet, json, etc.
- SQL database table design.
- Python + Postgres, data ingestion and retrieval.
- PySpark
- Data cleansing / dirty data.
You will need two things to work effectively on most all of these problems.
Dockerdocker-compose
All the tools and technologies you need will be packaged
into the dockerfile for each exercise.
For each exercise you will need to cd into that folder and
run the docker build command, that command will be listed in
the README for each exercise, follow those instructions.
The first exercise tests your ability to download a number of files
from an HTTP source and unzip them, storing them locally with Python.
cd Exercises/Exercise-1 and see README in that location for instructions.
The second exercise
tests your ability perform web scraping, build uris, download files, and use Pandas to
do some simple cumulative actions.
cd Exercises/Exercise-2 and see README in that location for instructions.
The third exercise tests a few skills.
This time we will be using a popular aws package called boto3 to try to perform a multi-step
actions to download some open source s3 data files.
cd Exercises/Exercise-3 and see README in that location for instructions.
The fourth exercise
focuses more file types json and csv, and working with them in Python.
You will have to traverse a ragged directory structure, finding any json files
and converting them to csv.
The fifth exercise
is going to be a little different than the rest. In this problem you will be given a number of
csv files. You must create a data model / schema to hold these data sets, including indexes,
then create all the tables inside Postgres by connecting to the database with Python.
Coming Soon!