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DNA Engineering Data Assignment

Build an ETL pipeline using Apache Airflow.

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow’s extensible Python framework enables you to build workflows connecting with virtually any technology. A web interface helps manage the state of your workflows. Airflow is deployable in many ways, varying from a single process on your laptop to a distributed setup to support even the biggest workflows.

Airflow Docs: https://airflow.apache.org/docs/

Prerequisites

Python 3.8 or higher
Create and activate a virtual environment

Virtual Environment

Creation

python -m venv venv

Activation

On Linux

source venv/bin/activate

On Windows

venv\Scripts\activate

Deactivation

deactivate

Project Setup

Export AIRFLOW_HOME before installing dependencies

export AIRFLOW_HOME="your_desired_airflow_location"

Install dependencies

pip install -r requirements.txt

Airflow Setup

Show Info about Airflow Env

airflow info

Display Airflow cheat sheet

airflow cheat-sheet

Set load_examples to False in airflow.cfg if you don't want to load tutorial dags and examples, before you execute the next command

Migrate airflow database

airflow db migrate

Create an Admin user

airflow users create \
    --username admin \
    --firstname first_name_example \
    --lastname last_name_example \
    --role Admin \
    --email your_email@example.com

Start all components

airflow standalone

Access Airflow UI at: http://localhost:8080, and enter your login information

Before we begin

In this assignment, you will be asked to write, refactor, and test code.
Make sure you respect clean code guidelines.
Read the assignment carefully.

Description

You are invited to build an ETL pipline using Ariflow in this assignment.
Data Location: source/jobs.csv

Data description

Your target data is located in the context column. It's a json data that needs to be cleaned, transformed and saved to an sqlite database

Provided by default:

Pipline structure with necessary tasks under dags/etl.py.
SQL Query for tables creation.
The blueprint task functions that needs to be completed.

Assignment

1. Code Refactoring

The code of the etl is grouped into one Python (dags/etl.py) script with makes it long, unoptimized, hard to read, hard to maintain, and hard to upgrade.

Your job is to:

Rewrite the code while respecting clean code guidelines.
Refactor the script and dissociate the tasks, and domains.

2. ETL Tasks

Fill in the necessary code for tasks: Extract, Transform, Load.

Extract job

Read the Dataframe from source/jobs.csv, extract the context column data, and save each item to staging/extracted as a text file.

Transform job

Read the extracted text files from staging/extracted as json, clean the job description, transform the schema, and save each item to staging/transformed as json file.

The desired schema from the transform job:

{
    "job": {
        "title": "job_title",
        "industry": "job_industry",
        "description": "job_description",
        "employment_type": "job_employment_type",
        "date_posted": "job_date_posted",
    },
    "company": {
        "name": "company_name",
        "link": "company_linkedin_link",
    },
    "education": {
        "required_credential": "job_required_credential",
    },
    "experience": {
        "months_of_experience": "job_months_of_experience",
        "seniority_level": "seniority_level",
    },
    "salary": {
        "currency": "salary_currency",
        "min_value": "salary_min_value",
        "max_value": "salary_max_value",
        "unit": "salary_unit",
    },
    "location": {
        "country": "country",
        "locality": "locality",
        "region": "region",
        "postal_code": "postal_code",
        "street_address": "street_address",
        "latitude": "latitude",
        "longitude": "longitude",
    },
}

Load job

Read the transformed data from staging/transformed, and save it to the sqlite database.

3. Unit Testing

As mentioned previously, your code should be unit tested.

Hints: Use pytest for your unit tests as well as mocks for external services.

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