A mid-sized analytics company faced major challenges managing a rapidly growing dataset of over 6 billion rows across 10 tables. With 20,000 new files arriving daily, their legacy infrastructure couldn’t keep up—resulting in slow processing, frequent system failures, and escalating operational costs.
Case Study: Big Data
in the Cloud
Streamlined processing of 6 Billion+ records
and eliminated frequent failures, cutting
costs and boosting reliability at scale.
The Challenge
The Solution
A Cloud-Native Pipeline Built for Scale, Speed & Sustainability
We architected a cloud-based data pipeline tailored to manage massive data growth with precision, speed, and cost efficiency.
Elastic Infrastructure for
High-Volume Processing
We deployed a scalable cloud environment with on-demand compute power, eliminating the need for over-provisioned resources and enabling efficient, high-speed data transformation at any scale.
Automated Ingestion
of 20K+ Daily Files
A distributed, event-driven pipeline ingests and processes tens of thousands of files daily. The system updates datasets in real time, ensuring accuracy and relevance without manual intervention.
Custom APIs for
Continuous Integration
We developed APIs that automate data ingestion, validation, and transformation, running daily to ensure seamless system updates and reliability.
Built-In Cost Controls Without
Performance Tradeoffs
By leveraging auto-scaling, spot instances, and query optimization, we slashed infrastructure costs while maintaining performance and reliability under growing data loads.
Results at a Glance
6 Billion
rows processed
Accelerated processing enabled reliable, daily data updates
Seamless scalability with automatic, load-responsive infrastructure adjustments
Cut costs to $650/month using cloud efficiency techniques
Built-in redundancy ensured high reliability and minimal downtime
Real Results. Real Impact.
By designing an architecture that scales dynamically and optimizes compute resources, we achieved high efficiency while keeping operational expenses minimal. This approach sets a precedent for other companies looking to manage large-scale data processing without incurring exorbitant infrastructure costs.
