Outbrain needed to upgrade its obsolete research cluster technology while maintaining seamless integration with on-premises infrastructure. The company faced tight timelines with only four months to choose, trial, and migrate before their hosting agreement renewal. Research cluster usage was elastic, leading to idle servers and wasted costs during smaller projects.
Outbrain partnered with DoiT International to migrate to Google Cloud using Cloud Dataproc as the backbone. The solution implemented autoscaling to adjust cluster size based on project requirements and used preemptible nodes for cost efficiency. BigQuery enabled granular traffic analytics, while Cloud Storage handled the 6 petabytes of research data.
In DoIT International, we found a partner we can really consult with and come up with radical new ways of how to do things. They have a deep understanding of the technologies involved. They're not just a contractor that executes our plan.
Orit Yaron, VP of Cloud Platform
Outbrain partnered with DoiT International to plan and execute the migration to Google Cloud. The project began with a Proof of Concept in late 2017 to test technology viability and train researchers. By January 2018, full migration commenced. The team transferred 6 petabytes of data to Cloud Storage using physical network backup lines to avoid disrupting normal operations. Cloud Dataproc formed the backbone of the new research cluster, providing integrated Hadoop features and seamless linking with Cloud Storage data.
The new architecture leveraged Cloud Dataproc with autoscaling capabilities to dynamically adjust cluster size based on project requirements. Preemptible nodes were implemented for cost efficiency during appropriate workloads. BigQuery enabled granular analytics on data center performance, allowing traffic optimization across regions. The Cloud Vision API provided image tagging capabilities at scale. This hybrid approach maintained integration with on-premises infrastructure while maximizing cloud benefits.
The migration delivered immediate cost benefits with 40% infrastructure cost savings compared to the previous research cluster. Project initiation became seamless, eliminating long hardware provisioning lead times. The company gained better cost predictability through autoscaling and on-demand cluster management. The modern technology stack improved talent acquisition by positioning Outbrain as a cutting-edge employer. Operational efficiency increased through serverless research capabilities and improved resource utilization.
Outbrain continues optimizing their service with Google Cloud technologies. The company is experimenting with Kubernetes clusters to handle traffic spikes more efficiently, enabling rapid compute scaling during news events and automatic shutdown when demand decreases. This approach addresses the challenge of over-provisioning servers for peak loads while maintaining service availability. The ongoing collaboration with Google focuses on long-term optimization and access to emerging tools and technologies.
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