ETL Pipeline for Retail Sales Data Integration

This repository implements an ETL (Extract, Transform, Load) pipeline to integrate sales data from various sources into a central data warehouse.

Problem

A retail company has sales data scattered across different systems:

• Point-of-sale (POS) systems (flat files)
• Online store database
• Customer relationship management (CRM) system

This fragmented data makes it difficult to get a holistic view of sales performance, customer behavior, and inventory management.

Solution

This ETL pipeline extracts data from each source, transforms it into a consistent format, and loads it into a data warehouse for analysis and reporting.

Technologies

• Python
• pandas (data manipulation)
• SQLAlchemy (database interaction)
• Airflow (workflow orchestration)

Project Structure

ETL_Data_Pipeline_For_Retail_Store/

• dags/
  • etl_dag.py
• data/
• utils/
• etl.py
• README.md

Implementation

Extract

Data is extracted from each source using appropriate libraries (e.g., pandas for CSV, SQLAlchemy for databases).

Transform

Data is cleaned, standardized, and transformed as needed. Reusable functions can be defined for specific transformations.

Load

Transformed data is loaded into the data warehouse using SQLAlchemy.

Running the Pipeline

1. Set up a virtual environment and install required libraries (python -m venv env && source env/bin/activate && pip install pandas sqlalchemy airflow).
2. Replace placeholder connection strings in etl.py with your actual source and destination connection details.
3. Initialize Airflow (airflow initdb).
4. Start the Airflow Web Server and Scheduler (airflow webserver -D and airflow scheduler -D).
5. Trigger the DAG manually or set a schedule in the DAG definition (dags/etl_dag.py).

Additional Notes

• Consider error handling, logging, and data lineage tracking for a robust production-ready pipeline.
• Explore cloud-based ETL services (e.g., AWS Glue, Google Cloud Dataflow) for scalability and managed infrastructure.

Getting Started

Clone this repository, set up the environment, and customize the connection strings for your data sources. Refer to the provided code comments and explore the scripts for more details.