Quantum Metric’s digital analytics platform processes millions of requests per second and ingests petabytes of data daily. When their platform team needed to migrate a core Google Kubernetes Engine (GKE) cluster to Dataplane V2, they needed to carefully avoid impacting the stability of critical services powering some of the world’s largest brands.
The goal: modernize without compromising performance.
The constraint: no in-place upgrade path.
The outcome: a seamless migration, with zero regressions.
The Stakes: Migrating a Backbone Cluster Under Load
Quantum Metric is the customer-centered digital analytics platform for today’s leading organizations, enabling global brands to make smarter, faster decisions about their digital customer experiences. Their platform ingests and processes petabytes of data each day to help these brands avoid revenue loss and deliver more seamless digital journeys across their web and mobile applications.
Operating at this scale presents numerous challenges and Quantum Metric’s engineering team is constantly looking for ways to best serve their customers. The team wanted to upgrade to Google Cloud’s Dataplane V2 as a way to address operational challenges related to networking, but the team knew this wouldn’t be a simple migration to execute. The cluster in question hosted some of their highest-throughput services, including internal request handlers and real-time data processing pipelines. Several of these services alone handled over 30,000 requests per second.
With no in-place upgrade available, success meant spinning up a new cluster, migrating services in stages, and deprecating the old one without impacting production.
Stepwise Migration with No Room for Error
The team adopted a blue-green migration strategy. They started by shifting lower-risk, stateless services with minimal dependencies, which was ideal for early validation. From there, they moved to heavier components with deeper integrations and higher throughput.
Every step introduced risk. The services being moved were foundational; any missed dependency or regression could create a cascade of downstream failures.
Why Causely Was Essential
For Kevin Ard, Staff Platform Engineer at Quantum Metric and leading the migration, confidence throughout the process came from one source: Causely.
“Because of Causely, I didn’t need to do any custom telemetry work or worry about if something was off as changes were rolled out. The system made it simple to complete our migration with confidence.”
Causely continuously builds a live model of service and data flows using lightweight, eBPF-based tracing - no manual instrumentation, dashboard-building or query-writing is required. As Kevin shifted traffic, Causely proactively showed and analyzed real-time changes in service dependencies and system health. If something started to show signs of degradation, it showed exactly where and why so that changes could be applied ahead of any major problems.
Example: Causely’s service and dataflow graphs show how requests and data move across services—and surface the true root cause directly within that flow.
Rather than stitching together dashboards or relying on intuition, Kevin was able to rely on Causely as a real-time copilot that analyzed cause-and-effect for him as changes were being made.
The Result: Two Weeks Saved, No Fire Drills
Without Causely, Kevin estimates he would’ve spent two weeks building dashboards, coordinating instrumentation with at least three other engineers, and piecing together a view of the system. Instead, he had a single, always-on copilot keeping an eye on things as traffic shifted.
“With the sheer volume of telemetry our systems can generate, filtering the noise to focus on causality was a game-changer. Knowing that Causely would proactively spot degradations without needing to configure any rules or alerts was icing on the cake.”
The result:
✅ Zero regressions
✅ No performance degradations
✅ No fire drills
✅ A smooth cutover of a high-throughput cluster
Making High-Scale Reliability Practical
Even in environments with strong observability, Causely adds something critical: real-time causal reasoning. It understands not just what changed, but why – and it does this automatically without custom dashboards or complex rules.
At Quantum Metric’s scale, reliability means preventing issues before they become incidents. During a high-risk migration, Causely gave Kevin and his team a new kind of clarity rooted in cause-and-effect across dynamic systems. This helped them improve how they think about managing complexity at scale and move fast without breaking things.
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