Background
A logistics technology provider required a reliable and automated ETL framework to process shipping and transactional data for its e-commerce partners. By leveraging AWS Glue jobs integrated with Amazon Aurora PostgreSQL, the company has modernised its data integration pipelines, reduced operational overhead, and accelerated delivery of analytics-ready data.
The Challenge
The company faced significant challenges with its legacy ETL approach:
- Manual scripts created inconsistencies in data quality.
- ETL pipelines lacked automation and monitoring, leading to delays.
- Scaling was difficult as shipping volumes fluctuated heavily.
- Analysts had limited access to trusted data in near real-time.
- High maintenance costs for infrastructure running data pipelines.
Our Solution
AWS Glue was selected to modernise the ETL framework.
- AWS Glue Jobs: Transformed raw shipping and partner data stored in Amazon S3 and ingested into Aurora.
- Glue Crawlers: Automated schema discovery and maintained metadata in the AWS Glue Data Catalog.
- Aurora PostgreSQL Integration: Used Glue’s JDBC connectors to load transformed datasets directly into Aurora tables.
- Job Orchestration & Monitoring: AWS Glue Studio provided job workflows while Amazon CloudWatch enabled performance monitoring and alerts.
- Scalable Serverless Processing: Glue automatically scaled with workload spikes during peak shipping seasons.
The Result
Automation: Replaced manual ETL scripts with automated Glue jobs, reducing manual intervention by 75%.
- Faster Data Availability: Reduced ETL cycle time from 4 hours to under 30 minutes.
- Improved Data Quality: Standardised validation and enrichment rules applied in Glue led to a 90% reduction in data inconsistencies.
- Scalability: Seamlessly handled seasonal order spikes without additional infrastructure.
- Business Enablement: Analysts gained near real-time access to high-quality operational data for logistics optimisation.
