GAP: Modern Data Architecture for Global Retail Operations

Objective

The objective of this project was to design and implement a Modern Data Architecture for GAP, enabling efficient global demand forecasting and operational insights across online and in-store transactions, inventory management, and supply chain activities.

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Business Impact

• Enhanced Inventory Management: Optimized stock levels and minimized overstock or understock scenarios.
• Improved Supply Chain Visibility: Facilitated real-time tracking of supply chain activities across global operations.
• Customer Insights: Improved decision-making with better customer behavior analysis through integrated CRM data.
• Scalability: Ensured a scalable and cost-efficient infrastructure capable of handling high data volumes.

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Architecture Process

Data Flow:

1. Data Sources: Real-time data streams from online and in-store transaction systems, CRM, and ERP systems.
2. Ingest:
   • Tools: Apache Kafka, Azure Data Factory, and AWS Glue.
   • Purpose: Handle real-time and batch data ingestion pipelines.
3. Store:
   • Tools: Azure Data Lake, Amazon S3, and Amazon RDS.
   • Purpose: Store raw and structured data for analytics and reporting.
4. Process & Train:
   • Tools: Apache Spark, Databricks, and AWS EMR.
   • Purpose: Enable real-time data processing and support machine learning model training.
5. Business User:
   • Tools: Power BI, QlikView, and custom applications.
   • Purpose: Dashboards, reporting, and personalized recommendations.

Additional Considerations:

• Data Governance: Ensured compliance with GDPR and regional data protection regulations using tools like Azure Purview and AWS Lake Formation.
• Security: Incorporated role-based access control (RBAC) and encryption.
• Scalability: Achieved auto-scaling and load balancing with Azure and AWS services.

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Technology Stack

1. Data Storage:

   • Hadoop: For distributed storage of large datasets.
   • NoSQL Databases: For scalable and flexible data types.
   • Data Warehouses: For structured transactional data.

2. Data Processing:

   • Apache Spark: For real-time analytics and data processing.
   • Kafka: For high-throughput data streams.

3. Data Visualization:

   • Power BI and QlikView: For dashboards and insights.

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Key Use Cases

1. Global Demand Forecasting:

   • Leveraged integrated data pipelines to generate accurate demand forecasts.
   • Improved decision-making for inventory management.

2. Customer Relationship Management (CRM):

   • Analyzed customer preferences and transaction patterns to drive personalized marketing.

3. Supply Chain Optimization:

   • Monitored supply chain performance to ensure timely deliveries.

4. Data Governance and Security:

   • Ensured compliance and data quality with robust governance measures.

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Scalability and Cost Optimization

• Scalability: Utilized geographically distributed Azure and AWS data centers to handle global data volumes.
• Cost Optimization:
  • Leveraged reserved instances and spot pricing.
  • Estimated Monthly Costs:
    • AWS: $22,560
    • Azure: $21,870
  • ROI: Increased revenue by $2,000,000 through sales growth and cost reductions.

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Conclusion

This project delivers a robust, scalable, and secure data architecture for GAP, ensuring operational efficiency and data-driven decision-making. By integrating advanced tools and adhering to best practices, this architecture is positioned to drive significant business value and enable GAP to thrive in a competitive retail environment.

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