Case Study

Logistics technology company Automates Data Integration with AWS Glue and Amazon Aurora PostgreSQL for Scalable Analytics

Rambunct Consulting Ltd. | AWS Advanced Tier Partner |

Background

Rambunct Consulting Ltd. | AWS Advanced Tier Partner |

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 modernized 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 modernize 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: Standardized 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 optimization.