Datadog > Case Studies > Powering the Growth of a High-Volume Video Advertising Platform

Powering the Growth of a High-Volume Video Advertising Platform

Customer Company Size

Mid-size Company

Country

United States

Product

SpringServe
Datadog
AWS Kinesis
Amazon Elastic Load Balancing (ELB)

Tech Stack

Cloud Computing
Real-time Analytics
Data Streaming

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Revenue Growth
Productivity Improvements
Customer Satisfaction

Technology Category

Platform as a Service (PaaS) - Data Management Platforms

Applicable Functions

Sales & Marketing
Business Operation

Use Cases

Real-Time Location System (RTLS)
Predictive Quality Analytics

Services

Cloud Planning, Design & Implementation Services
Data Science Services

About The Customer

SpringServe is a video ad server platform that handles tens of billions of ad requests per day. The platform collects video advertising opportunities from a network of publishers, distributes them to real-time bidders, and serves the winning ads on the publishers’ properties—all in under 100 milliseconds. Demand and supply-side customers are then able to optimize their current and future advertising allocations with SpringServe’s dashboard-rich UI, which provides a transparent view of the market through granular, real-time campaign metrics. SpringServe's business hinges on its ability to quickly and consistently deliver video advertisements during varying levels of demand. Failure to respond to an advertising opportunity in a timely manner translates to lost revenue for SpringServe and the publisher, a missed opportunity for the advertiser, and a likely win for a competing ad platform.

The Challenge

SpringServe, a video ad server platform, was facing challenges with its self-hosted monitoring stack which could not correlate metrics between systems, making it difficult to identify and resolve issues before they directly impacted the customer experience. As SpringServe's infrastructure was becoming more dynamic and distributed to provide better service around the world, significant blind spots hampered those efforts. They could not track application performance across regions, nor could they correlate metrics between systems to uncover the source of issues. SpringServe needed a reliable, real-time monitoring solution that could keep pace with its auto-scaling infrastructure, allow them to adopt innovative technologies, and keep growing quickly—without sacrificing the speed or consistency their customers depend on.

The Solution

SpringServe turned to Datadog for real-time, granular data that allows them to monitor every layer of their infrastructure and custom applications, identifying issues before they affect the business. Datadog automatically scales with SpringServe’s infrastructure footprint and correlates metrics from various applications, enabling engineers to quickly investigate issues. With full visibility into their stack, SpringServe’s teams are now able to devote more time to delivering innovative products and improving customer experience. Datadog’s built-in integrations enabled SpringServe to gain immediate visibility into their most critical systems, allowing them to readily prevent performance and capacity issues. By tracking every stage of the ad-serving process in Datadog, SpringServe is able to proactively reduce risk and latency throughout their platform and increase the value they provide to customers over time.

Operational Impact

Datadog’s automatic correlation of metrics and events helps engineers resolve issues faster.
SpringServe uses alerts to proactively identify Elastic Load Balancing latency issues before they lead to lost revenue.
SpringServe can now analyze business metrics to identify areas of the platform that are ripe for improvement.
With reduced MTTR, and without the burden of managing a self-hosted monitoring stack, SpringServe engineers are now able to focus on enhancing their platform’s value-producing offerings.

Quantitative Benefit