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Drift Detection

Drift Prevention vs Detection: Does a Polling Approach make sense At Scale?

Many organizations typically rely on pull-based GitOps tools (e.g. Argo CD) to detect and remediate drift on their Kubernetes clustes. This approach allows clusters to diverge before reconciling them on the next polling interval. For the last 4 years, Rafay customers have benefited from an architecturally different approach that focuses on true drift prevention, backed by robust detection capabilities across both cluster blueprints and application workloads.

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In a previous blog, we discussed how ArgoCD's reconcilation works and its best practices.

Drift Block

Cluster Blueprints and Drift Detection

Around three years back, we noticed many of our customers struggling with enterprise wide standardization of their Kubernetes clusters. Every cluster in their Organization was a snowflake and they were looking for a way to enforce that every cluster had a "baseline set of add-ons". This prompted us to develop Cluster Blueprints which has turned out to be one of the most heavily used features in our platform.

In this blog, we will describe a superpower setting in the cluster blueprints feature that we see customers use heavily for their production clusters to secure against unplanned drift.

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