Optimized database strategy
Machine learning insights
New SKU combinations
Improved operational efficiency
The challenge
Defining a scalable cloud migration strategy for real-time retail platforms
A multi-national retailer sought a migration strategy and roadmap for their cloud journey, aiming to build a next-generation architecture to support their real-time platforms, including their online store.
Key challenges
Migrating and consolidating data
Need for modernization and scalability
Supporting high-traffic, real-time transactions
Building adaptable infrastructure for future growth
The solution
Holistic solution framework for mission-critical data migration to GCP
Data integration & replication
Migrated data to GCP
Built sales data pipelines
Integrated ML for insights
Real-time insights
Developed models for insights
Enabled REST APIs
Integrated across platforms
Implementation approach
1
Data pipeline setup
Automated data ingestion to GCP
Ensured real-time sales flow
Used GCP tools for fast processing
2
Machine learning models
Developed predictive models
Trained on historical data for trends
Enhanced decisions with analytics
3
API & security
Built REST APIs
Managed flow using Cloud composer
Secured access with Cloud endpoint
The impact
Optimized operations and data-driven insights for retail growth
Operational efficiency
Optimized database partitioning
Reduced resource usage
Improved scalability
Data-driven insights
Applied ML to customer behavior
Introduced SKU combinations
Enhanced customer satisfaction
Business growth
Boosted revenue
Enhanced inventory management
Strengthened retail strategy
Looking ahead
Enhanced data scalability
Continuous optimization of data infrastructure to support growing transaction volumes
Advanced AI integration
Leveraging machine learning for deeper insights into customer behavior and trends
Seamless multi-platform integration
Expanding the API ecosystem to enhance integration across mobile, web, and in-store applications
