Job Data ELT Pipeline

Table of Contents

• Overview
• Features
• Architecture
• Installation
• Usage
• Contributing
• Acknowledgements

Overview

This project is an ELT (Extract, Load, Transform) data pipeline that fetches job data from the RapidAPI service, processes it, and stores it in a MongoDB database. The aim is to create a robust and scalable data pipeline for job market analysis.

Features

• Data Extraction: Fetches job data from RapidAPI.
• Data Loading: Stores raw data into MongoDB.
• Data Transformation: Processes and transforms the raw data into a structured format.
• Scheduling: Automates the data pipeline to run at regular intervals.
• Error Handling: Implements robust error handling mechanisms to ensure data integrity.

Architecture

1. Data Extraction: Uses RapidAPI to fetch job data.
2. Data Loading: Loads the raw job data into MongoDB.
3. Data Transformation: Cleans and structures the data for analysis.
4. Data Storage: Stores the transformed data back into MongoDB for querying and analysis.

The high-level architecture of the pipeline is as follows:

+-----------------+       +-----------------+       +-------------------+
|                 |       |                 |       |                   |
|   RapidAPI      +------>+   ETL Process   +------>+   Transformed     |
|   (Job Data)    |       |  (Clean Data)   |       |   Data (MongoDB)  |
|                 |       |                 |       |                   |
+-----------------+       +-----------------+       +-------------------+

Installation

Prerequisites

• Python 3.8 or higher
• MongoDB
• RapidAPI account

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/job-data-elt-pipeline.git
   cd job-data-elt-pipeline

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Set up your MongoDB database and RapidAPI credentials.

Usage

1. Configure the pipeline: Update the config.json file with your MongoDB and RapidAPI details.

2. Run the pipeline:

   python main.py

3. Scheduling: Use a task scheduler like cron (Linux/macOS) or Task Scheduler (Windows) to automate the pipeline.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.

Acknowledgements

• Thanks to RapidAPI for providing the job data API.
• Special thanks to all contributors and supporters of this project.