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How we optimized production data pipelines for Jubilant FoodWorks, reducing execution time by 66% and data storage costs by 50%
April 2025 - Present • 1 month
Client
Company
Personal Project
Category
Ongoing
Duration
1 month
Project Overview
In the fast-paced food service industry, efficient data operations are critical for business success. This case study explores how we developed, maintained, and optimized production data pipelines for Jubilant FoodWorks, the exclusive master franchisee for Domino's Pizza in India, on AWS Infrastructure. Our optimization efforts led to significant improvements in execution time and substantial cost savings on data storage.
The Challenge
Jubilant FoodWorks, operating one of India's largest quick-service restaurant chains, faced several challenges with their existing data pipeline infrastructure:
- Lengthy data processing times impacting business agility
- High data storage costs on Amazon Redshift
- Inefficient ETL processes causing bottlenecks
- Scalability issues during peak business hours
- Growing data volume from expanding restaurant operations
The company needed a robust solution to optimize their data pipelines, reduce costs, and improve overall system performance while maintaining data integrity and accessibility for business users.
Our Solution
We designed and implemented a comprehensive pipeline optimization strategy on AWS that included:
1. Pipeline Architecture Redesign
We conducted a thorough assessment of the existing pipeline architecture and redesigned critical components to eliminate bottlenecks and improve data flow. This included restructuring the data ingestion process and implementing parallel processing where appropriate.
2. ETL Process Optimization
Using AWS Glue and PySpark, we refactored the ETL processes to improve efficiency and reduce processing time. We implemented partitioning strategies, optimized transformations, and enhanced error handling to ensure reliable data processing.
3. Redshift Storage Optimization
We implemented advanced compression techniques, table optimization, and data archiving strategies to reduce storage requirements in Amazon Redshift. We also redesigned the data distribution strategy to optimize query performance while minimizing storage costs.
4. Automated Monitoring and Maintenance
We developed automated monitoring tools using AWS CloudWatch and Lambda functions to proactively identify and address potential issues before they impacted production. We also implemented routine maintenance procedures to ensure ongoing optimal performance.
Technical Stack
Key Results
66%
Reduction in production pipeline execution time
50%
Decrease in data storage costs on Amazon Redshift
99.9%
Pipeline reliability and uptime
40%
Improvement in query performance for business users
Our pipeline optimization project delivered significant benefits for Jubilant FoodWorks:
- Faster data availability enabling more agile business decisions
- Substantial cost savings through storage optimization
- Improved scalability to handle growing data volumes
- Enhanced system reliability with fewer pipeline failures
- Better resource utilization across the AWS infrastructure
- Improved data accessibility for business intelligence teams
Conclusion
The successful optimization of Jubilant FoodWorks' production data pipelines demonstrates the significant impact that well-designed data engineering solutions can have on business operations. By reducing processing times by 66% and cutting storage costs in half, we helped the client achieve better performance while simultaneously reducing operational expenses.
This project showcases our expertise in AWS cloud infrastructure, data pipeline optimization, and cost-efficient data engineering practices. The solutions we implemented continue to support Jubilant FoodWorks' growing operations and data needs while providing a foundation for future analytics initiatives.
Need to Optimize Your Data Pipelines?
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