Multi-Channel Retail Data Platform

Project Overview

Implemented a comprehensive multi-channel retail data platform that integrated sales data, product information, store operations, purchase orders, and invoicing data across the enterprise. The solution was built using Azure Data Factory and Synapse Analytics to handle complex batch processing requirements.

The platform enabled real-time visibility into retail operations, providing stakeholders with actionable insights for inventory management, sales optimization, and supply chain efficiency.

Key Features

• Multi-Source Integration: Consolidated data from sales systems, product databases, store networks, and supply chain platforms
• Batch Processing Pipelines: Built scalable batch processing pipelines handling millions of transactions daily
• Sales Analytics: Real-time sales data analysis across multiple channels and geographic regions
• Inventory Management: Automated inventory tracking and reorder point calculations
• Purchase Order Processing: Streamlined purchase order workflows and tracking
• Invoice Management: Automated invoicing data processing and reconciliation

Technologies Used

• Azure Data Factory (ADF): Pipeline orchestration and data movement
• Azure Synapse Analytics: Data warehousing and analytics
• Azure Data Lake Storage (ADLS): Centralized data storage
• SQL Server: Relational data storage and processing
• Power BI: Business intelligence and reporting

Technical Implementation

The solution architecture leveraged Azure Data Factory for orchestrating data movement from various source systems into Azure Data Lake Storage. Data was then processed through Azure Synapse Analytics, where complex transformations were applied to create business-ready datasets.

Batch processing pipelines were scheduled to run at optimal times to minimize impact on operational systems while ensuring data freshness for reporting and analytics.

Data Flow Architecture

1. Ingestion Layer: ADF pipelines extract data from multiple source systems
2. Storage Layer: Raw data lands in ADLS Gen2 with appropriate partitioning
3. Processing Layer: Synapse Analytics performs transformations and aggregations
4. Serving Layer: Curated data made available for BI tools and applications

Challenges & Solutions

• Data Volume: Handled large transaction volumes through optimized partitioning and parallel processing
• Data Quality: Implemented validation rules and data quality checks at each processing stage
• System Integration: Created robust connectors for legacy systems with varying data formats
• Performance: Optimized SQL queries and data movement operations for faster processing

Business Impact

The retail data platform delivered measurable business value:

• Improved inventory accuracy reducing stockouts and overstock situations
• Enhanced sales visibility across all channels
• Faster purchase order processing and vendor management
• Better demand forecasting through integrated sales and inventory data
• Reduced manual data reconciliation efforts

My Role

As Azure Data Engineer, I designed the overall data architecture, developed ADF pipelines for data ingestion, implemented transformation logic in Synapse Analytics, and created automated batch processing workflows. I collaborated with business users to understand reporting requirements and ensured data accuracy and timeliness.