To run

• git clone https://github.com/goutham794/data-engg.git
• cd data-engg
• mkdir app_logs
  We need an app_logs directory, this folder would be bind mount to the container, so we can view generated logs locally.
• docker-compose up -d

To view the inserted data

• docker exec -it my_mongo mongosh
  Now you get access to the mongo shell in the mongodb docker container.
• use EmployeeManagement
• db.Employees.find()
  The above command displays all inserted documents in the Employees collection.

Logs

• Look in app_logs/ folder to see the logs of cleaning and transformation done.

Tests

Overview

This suite of tests, located in tests/test_main.py, is designed to validate the data cleaning, transformation, and loading processes of our data pipeline. The suite specifically tests the read_csv, clean_data, transform_data, and load_data functions from the main module.

Running tests locally

Setup local py env (3.8+)

• python -m venv env
• source env/bin/activate [FOR LINUX/MAC]
• .\env\Scripts\activate [WINDOWS]
• pip install -r requirements.txt
• pytest -v tests/test_main.py

Test Structure

The tests are structured as follows:

Reading Data:

• test_read_csv: Checks if data is read correctly from a sample CSV and returns a DataFrame.

Data Cleaning:

• test_name_cleaning .
• test_date_cleaning

Data Transformation:

• test_name_merging
• test_age_calculation
• test_salary_bucket_allocation

Data Loading:

• test_load_data: Tests MongoDB insertion by ensuring that the (mock) db client is called with the right data and arguments.

Documentation

Data Transformation and Cleaning

• Column header names are stripped of trailing spaces
• Stripping White Spaces in all columns.
• Removing rows where values match the column header names.
• Trailing and Leading non-alphabet characters are removed from the names.
• Misaligned rows (values shifted by 1 column), due to merged first and last names, are fixed.
• Birth date is cleaned (YYYY-MM-DD followed by random string is cleaned)
• Period or decimal point found in Birth date is removed.
• Rows with Birth date <1900 or > 2015 are removed.

Data Loading

The load_data function is designed to take a given DataFrame and store its records into a specified MongoDB collection.

Index is created on the EmployeeID field of the collection to ensure uniqueness and improve search performance.

Assumptions

• Trailing and Leading non-alphabet characters are removed from the names. Non-alphabet characters inside the name are kept.
• Rows containing salaries with any non-numeric characters or negative numbers are deleted.