Client
A leading provider in the waste management sector, delivering solutions for efficient waste collection, recycling, and disposal to enhance operational efficiency & sustainability.
Business Challenge
- Unintegrated data sources cause scattered reporting and manual consolidation.
- Decentralized platform hinders data management and access control.
- Manual data processes lead to delays and inconsistencies.
- Disjointed platform needs modernization for real-time fleet data.
- Lack of advanced analytics limits proactive decision-making.
Business Objective
- Consolidate diverse data sources into an AWS S3 data lake for a single source of truth.
- Leverage Amazon S3's scalability and durability for efficient data storage and management.
- Utilize AWS analytics services for big data processing and machine learning capabilities.
- Enable real-time data ingestion and processing
- Enhance decision-making through insights from KPI’s and optimized operations.
Approach
- Delivered a fully-integrated, AWS-native data and analytics platform for real-time decision-making.
- Designed the platform following AWS's Well-Architected Framework for a secure data foundation.
- Built a robust data lake using AWS Glue for seamless data integration & management.
- Implemented serverless, event-driven architecture with AWS Lambda for automated data processing.
- Integrated diverse data sources, including IoT, fleet management, & ERP systems.
- Utilized AWS Glue to catalog datasets for efficient metadata analysis & schema management.
- Developed 360-degree analytics around customers, vehicles, and drivers using AWS services.
- Monitor performance and costs with CloudWatch for proactive infrastructure management.
Technology Components
- Compute: AWS Lambda, AWS Glue
- Data Integration: AWS Glue, AWS CodePipeline
- Storage: Amazon S3, Amazon Redshift
- Analytics: Amazon Athena, Amazon QuickSight
- Monitoring: Amazon CloudWatch
Transformational Effects
- Achieved over 25% cost savings by utilizing Amazon S3 intelligent Tiering for data storage, cleaning & curation
- Saved $80K+ in license costs by migrating workloads from Cloud ETL to AWS Glue and Kinesis
- Reduced issue resolution time by 40% through automated monitoring of Glue workloads
- Reduced operational costs by an estimated 30% via adoption of serverless technologies
