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.
- 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.
- Data Extraction: Uses RapidAPI to fetch job data.
- Data Loading: Loads the raw job data into MongoDB.
- Data Transformation: Cleans and structures the data for analysis.
- 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) |
| | | | | |
+-----------------+ +-----------------+ +-------------------+
- Python 3.8 or higher
- MongoDB
- RapidAPI account
-
Clone the repository:
git clone https://github.com/yourusername/job-data-elt-pipeline.git cd job-data-elt-pipeline
-
Create and activate a virtual environment:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
-
Install the required packages:
pip install -r requirements.txt
-
Set up your MongoDB database and RapidAPI credentials.
-
Configure the pipeline: Update the
config.json
file with your MongoDB and RapidAPI details. -
Run the pipeline:
python main.py
-
Scheduling: Use a task scheduler like
cron
(Linux/macOS) orTask Scheduler
(Windows) to automate the pipeline.
Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.
- Thanks to RapidAPI for providing the job data API.
- Special thanks to all contributors and supporters of this project.