Key Highlights: What This Case Study Covers
- Best practices in data engineering services for global retail, including automated data pipelines, cost optimization, and governance frameworks.
- Successful data migration services using AWS Glue, S3, and Redshift to build a scalable analytical data lake and enable advanced reporting.
- How a leading retailer partnered with a data engineering consulting company to automate file ingestion, transformation, and auditing workflows.
- Real-world application of cloud implementation services with AWS Lambda, DynamoDB, and QuickSight for faster insights and interactive dashboards.
- Strategic data modernization solutions that improved decision-making speed while optimizing storage and processing costs.
Client Overview
Our client is a Canadian-headquartered global retailer operating nearly 3,000 footwear and accessories stores across 100+ countries.
The Ask
The client wanted to modernize its data infrastructure to support advanced analytics and dashboards while ensuring scalability, cost efficiency, and strong data governance.
Challenges
- Cloud Migration at Scale: While the client had a well-established transactional database, they needed a secure and efficient path to migrate this data into AWS without disrupting existing operations.
- Manual Data Integration: Monthly file preparation was heavily manual, creating bottlenecks for teams that required timely and consistent data for analytics.
- Limited Agility in Reporting: Existing reporting processes were static, making it harder for business users to quickly explore data or adapt insights to fast-changing retail needs.
Our Solution: AWS-Powered Data Modernization and Automation
- Built Secure Data Pipelines: Orchestrated AWS Glue jobs to ingest data from the client’s SAP transactional database into Amazon S3. Captured auditing details in DynamoDB and enabled downstream processing via AWS Lambda.
- Enabled Scalable Analytics: Established analytical layers using Athena catalog tables and Redshift, allowing business users to query large datasets seamlessly and support advanced reporting.
- Automated Monthly File Processing: Implemented a user-centric ingestion process for CSV uploads. Lambda functions triggered AWS Glue jobs to transform raw data, load it into prepared layers, and log activity in DynamoDB for traceability.
- Streamlined Infrastructure as Code: Used AWS CDK with Bitbucket pipelines to provision and manage resources automatically, ensuring consistency and agility in deployments.
- Delivered Interactive Dashboards: Leveraged Amazon QuickSight to design and deploy dashboards tailored to business functions, enabling self-service analytics and faster decision-making.
Impact Delivered
- Fresher Data, Faster Insights: Enabled quicker updates for always-available, real-time decision-making.
- Cost Optimization: Applied AWS cost-saving techniques like lifecycle rules, Glue auto-scaling, and optimized pipelines.
- Proactive Monitoring: Automated notifications for pipeline issues enabled faster troubleshooting.
- Faster Analysis: Optimized data partitioning and processing reduced query response times.
- Optimized Storage: Automated archiving of redundant data reduced storage costs.
