case studies

Optimizing Data Processing Efficiency

Executive Summary

GeC significantly enhanced its data processing efficiency by migrating its architecture to S3-based Athena/Iceberg tables and automating workflows. This modernization led to a 10x improvement in performance and expanded export capacity by 300x, reducing operational overhead and enhancing scalability.

Project Objective

Eliminate inefficient data ingestion and export.
Centralize scattered data sources across systems.
Implement centralized monitoring and queriable logs.
Resolve infrastructure scalability bottlenecks.

About Customer

Global eContent (GeC) specializes in providing tenders information services across industries and geographies. Trusted by service providers worldwide, GeC aggregates and delivers critical information through a team of dedicated researchers and specialists.

The Solution

Scalable Data Management

Transitioned from PostgreSQL to an S3-based Athena/Iceberg architecture, improving data querying efficiency and governance.

Automated Data Processing

Introduced centralized workflows to automate web scraping, analytics, and data exports, eliminating manual dependencies.

Dynamic Infrastructure Scaling

Deployed automated scaling for EC2 instances, ensuring optimal system performance and cost reduction based on real-time demands.

The Benefits

Enhanced Efficiency

10x improvement in data processing speed.
Ensured faster and more reliable data ingestion.
Optimized task execution.

Improved Scalability

Enabled seamless scalability with S3-based architecture.
Dynamic resource allocation reduced unnecessary costs.
Reduced infrastructure costs.

Better Governance

Centralized data storage improved visibility and control.
Real-time dashboards enhanced operational oversight.
Automated reporting streamlined compliance.

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