Modernizing Data Engineering for a Leading Quick Service Restaurant Brand
A leading quick service restaurant (QSR) company operating across North America needed a scalable, cloud-native data engineering platform. The client aimed to consolidate data from multiple ERPs and PoS systems into a centralized architecture, while optimizing for cost, performance, and real-time analytics. Their legacy systems lacked the agility and efficiency needed to support growing business intelligence demands.
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Challenge
Fragmented systems and static infrastructure were driving up costs and reducing efficiency. The client needed a high-performance, cloud-optimized architecture with flexible scaling and automation.
Inefficient Data Integration and Processing
Costly and Inflexible Infrastructure
Solution
Innovation Strategy
Entrans developed a modern data engineering platform using AWS services. We designed a curated data lake and integrated Amazon Redshift for analytics, with Amazon EMR and Athena handling semi-structured and batch processing workloads.
Collaborative Approach
Our AWS-certified engineers worked directly with the client’s IT and BI teams to build custom data marts, optimize performance, and automate deployment pipelines for seamless updates and monitoring.
Key Initiatives
- Implemented AWS Redshift and Amazon S3 for scalable storage and warehousing
- Used Amazon EMR and Athena for cost-efficient data transformation and querying
- Automated CI/CD using GitLab, Jenkins, Octopus Deploy, and AWS native tools
- Created region-specific data marts to improve reporting agility
Business Transformation
With optimized storage, compute, and analytics workflows, the client can now support real-time decision-making, reduce IT overhead, and scale to meet seasonal business demands.
Future-Ready
The AWS-based platform allows the client to expand across regions, integrate AI/ML use cases, and pursue predictive insights with minimal technical debt.
Client Quote
"Entrans’ AWS-based data engineering platform has transformed our data operations, ensuring scalability, cost-efficiency, and real-time analytics."
— Data Lead, Confidential Quick Service Restaurant
Key Takeaways
- Delivered a cloud-native, pay-per-use data infrastructure on AWS
- Reduced query times by over 90% and cut operational costs
- Built CI/CD-enabled data workflows supporting future growth
Outcomes
Query execution time reduced from minutes to milliseconds
Integrated data across ERP and PoS systems for end-to-end visibility, enabling faster business insights through real-time reporting dashboards
Pay-per-use model significantly reduced infrastructure costs
