Case Study: PubMatic cuts HDFS block footprint 30% and saves millions in licensing with Acceldata
Key results
The challenge
PubMatic, one of the largest U.S. AdTech companies, serving roughly 200 billion daily ad impressions and processing more than 2 petabytes of new data per day, experienced excessive MTTR, frequent outages, and performance bottlenecks driven by its massive scale, including as many as 1,500 nodes in a single cluster. It also carried high infrastructure and OEM support costs it wanted to reduce.
The solution
The Acceldata platform isolated bottlenecks, automated performance improvements, and distinguished between mandatory and unnecessary data to scale PubMatic's big-data environment reliably. This correlated events across infrastructure, data layers, and pipelines to support its mission-critical and customer-facing analytics.
The results, in context
HDFS optimization reduced PubMatic's block footprint by 30%, Kafka cluster consolidation saved infrastructure costs, and reduced OEM support costs saved millions of dollars per year in software licenses. Acceldata also eliminated day-to-day engineering firefighting on outages and performance issues, letting data teams focus on growing the business.