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Data Engineering Practice Problems

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.

*** Note - If you email a link to your GitHub repo with all the completed exercises, I will send you back a free copy of my ebook Introduction to Data Engineering. ***

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.

How to work on the problems.

You will need two things to work effectively on most all of these problems.

  • Docker
  • docker-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.

Beginner Exercises

Exercise 1 - Downloading files.

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.

Exercise 2 - Web Scraping + Downloading + Pandas

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.

Exercise 3 - Boto3 AWS + s3 + Python.

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.

Exercise 4 - Convert JSON to CSV + Ragged Directories.

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.

Exercise 5 - Data Modeling for Postgres + Python.

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.

Intermediate Exercises

Exercise 6 - Ingestion and Aggregation with PySpark.

The sixth exercise Is going to step it up a little and move onto more popular tools. In this exercise we are going to load some files using PySpark and then be asked to do some basic aggregation. Best of luck!

Exercise 7 - Ingestion and Retrieval with ElasticSearch.

*** IN PROGRESS ** The seventh exercise Again, we are going to try a project with another popular Big Data tool, namely ElasticSearch. Very different from the last project with PySpark, but this exercise will require more attention to detail and fine-tuning. You will ingest a .txt file into a locally running ElasticSearch instance and then retrieve some information from what you just stored.