Our adtech client needed a robust and scalable solution to manage their data ingestion, processing, and storage. The existing system struggled to handle the massive volume of data generated daily, leading to inefficiencies and escalating costs. The client required a scalable infrastructure capable of near-realtime processing, efficient data storage, and seamless integration with reporting and business intelligence tools.

We assembled a team of four engineers to work closely with the client’s two onsite engineers and product lead. A data ingestion and processing pipeline was designed on Amazon AWS to handle massive data volumes. Autoscaling using Amazon ECS dynamically adjusted ingestion services based on traffic volume. The data transformation and aggregation engine, built with AWS Glue and Apache Spark, enabled near-realtime processing. A robust data warehouse on AWS Redshift supported multi-terabyte storage and analytics. A custom REST API with Django provided easy access to reports and integration with business intelligence tools. Historical raw data was stored on AWS Glacier to reduce costs. Infrastructure setup was streamlined using CloudFormation, enabling deployment in no more than 1-2 hours. Monitoring and log analytics were integrated with Datadog, and on-call support was provided with PagerDuty to address system issues promptly.

The project delivered huge improvements to the client’s data infrastructure. The new system processes over 400 million HTTP events via HeaderBidding daily, scaling automatically to traffic fluctuations, and handles more than 1TB of data efficiently. Near-realtime data transformation and aggregation enable faster insights, while the data warehouse supports multi-terabyte data storage and advanced analytics. Monitoring and alerting enhanced system reliability, while efficient storage solutions reduced costs for historical data. The client now benefits from a streamlined, cost-effective, and scalable platform that supports their business needs.