Retail-Data-Pipeline

This repository contains the code and documentation for a robust data pipeline that ingests, transforms, and loads retail data from various sources into a data warehouse. The pipeline is built using popular open-source tools and frameworks, making it scalable and easy to maintain.

Key Features

• Data Ingestion: Supports ingestion from multiple data sources, including CSV files, SQL databases, and APIs.
• Data Transformation: Applies a series of transformations to clean, enrich, and standardize the data using Apache Spark.
• Data Loading: Loads the transformed data into a data warehouse (e.g., Snowflake, BigQuery, Redshift) for analysis and reporting.
• Orchestration: Uses Apache Airflow to orchestrate the end-to-end data pipeline, ensuring reliable and scheduled execution.
• Monitoring and Alerting: Implements comprehensive monitoring and alerting to proactively detect and address pipeline issues.
• Scalability: Designed to handle growing data volumes and adapt to changing business requirements.

Getting Started

To get started with the Retail Data Pipeline, please follow these steps:

1. Prerequisites:

   • Install Docker and Docker Compose on your local machine.
   • Ensure you have access to the necessary data sources (e.g., SQL databases, APIs) and a data warehouse (e.g., Snowflake, BigQuery, Redshift).

2. Clone the Repository:

   git clone https://github.com/MuziZwane/Retail-Data-Pipeline.git
   cd Retail-Data-Pipeline
   

3. Configure the Pipeline:

   • Update the config.py file with your data source and data warehouse credentials and settings.
   • Customize the data transformation logic in the transform.py file to meet your specific requirements.

4. Deploy the Pipeline:

   docker-compose up -d
   

   This will start the Airflow webserver and scheduler, as well as the other services required for the pipeline.

5. Monitor the Pipeline:

   • Access the Airflow webUI at http://localhost:8080 and observe the pipeline's execution.
   • Configure email or Slack alerts to receive notifications about pipeline failures or other critical events.

Contributing

If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request. We welcome contributions to help make the Retail Data Pipeline even better!

License

This project is licensed under the MIT License.