Rethinking Data Virtualization with On-Demand Caching and PlainID Copy

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ata virtualization has emerged as a pivotal strategy for large enterprises seeking a unified identity governance framework. Many solutions rely on caching, aggregation, and correlation to deliver quick, context-rich data for downstream decision engines. However, these caching schemes often falter when the *potential domain of query parameters*—the range of values you might need to fetch—grows too unwieldy or shifts unpredictably.

An industry-leading network equipment and enterprise software provider found itself contending with this exact issue. Key metadata lived behind a sluggish API, and each request triggered a costly lookup. The result? A transaction bottleneck that forced the policy decision point (PDP) to sit idle while external calls ran their course.

The fix? A *lazy caching* approach, featuring a constrained cache size and an LRU (Least Recently Used) eviction policy. Instead of loading everything in advance, the system fetched data only as needed, delivering swift response times and avoiding the complexity of massive cache-building processes.

Enter *PlainID*: By offering on-demand data retrieval and dynamic caching capabilities, PlainID’s Policy Information Point (PIP) helps enterprises manage vast and variable data domains. With PlainID, policies are enforced accurately and efficiently, without overloading resources. The lesson? Even when you’re dealing with a vast range of query parameters, a carefully orchestrated lazy caching strategy—powered by robust policy-based access—keeps your identity services flexible, scalable, and secure.