ETL Pipeline for E-commerce Data

Skills, Tech Stack, and Libraries

1. Skills: ETL Workflows, Data Integration, Data Cleaning, Data Transformation, Real-Time Analytics

2. Tech Stack: Python, SQL, AWS (Glue, Redshift, S3), Azure Data Factory, Snowflake

3. Libraries: Pandas, NumPy

Approach

Objective:

I developed an ETL (Extract, Transform, Load) pipeline to process and centralize e-commerce transactional data from multiple sources, enabling seamless integration, data analysis, and real-time reporting for improved business decision-making.

Approach:

1. Data Extraction:

   • Connected to various data sources, including SQL databases, APIs, and CSV files.

   • Extracted raw data using Python scripts and Azure Data Factory, ensuring data was fetched efficiently and regularly.

2. Data Transformation:

   • Cleaned and standardized the data using Pandas to handle inconsistencies such as missing values, duplicate entries, and incorrect formats.

   • Merged data from multiple sources and applied business logic to create unified tables.

   • Created calculated fields like revenue per transaction, customer lifetime value, and product profitability to enrich the dataset.

3. Data Loading:

   • Loaded the transformed data into a centralized Snowflake data warehouse for analytical processing.

   • Optimized storage with partitioning and indexing strategies to improve query performance.

4. Real-Time Updates:

   • Implemented real-time or near real-time data ingestion and transformation workflows using AWS Glue to update dashboards dynamically.

5. Visualization and Reporting:

   • Enabled business users to analyze key metrics by connecting tools like Tableau and Power BI directly to the data warehouse.

     
     

Code Flow:

1. Extract raw data from sources (databases, APIs, flat files) using Python and Azure Data Factory.

2. Clean and preprocess data using Pandas, applying business logic for standardization and enrichment.

3. Load processed data into Snowflake or AWS Redshift for centralized storage.

4. Build and update dashboards using Power BI or Tableau to visualize trends and insights.

Results

The ETL pipeline provided significant improvements in data integration and reporting, including:

• Centralized Data Access: Unified data from multiple sources into a single, accessible location, improving cross-departmental collaboration.

• Improved Reporting Speed: Reduced data processing time by 50%, enabling near real-time reporting for critical metrics like sales trends and inventory levels.

• Actionable Insights: Delivered enriched data with calculated metrics, enhancing business decision-making on pricing, inventory, and customer targeting.

• Scalable Solution: The pipeline architecture supported integration with additional data sources, ensuring adaptability for future business needs.

This project demonstrated the efficiency and flexibility of ETL pipelines in modernizing data management and analytics for e-commerce platforms.

Git Link

For more information and code, visit the Git link.