<![CDATA[This paper proposes a four-tier telemetry ingestion architecture that integrates adaptive sampling, backpressure-aware streaming, schema-evolution-tolerant storage, and independent telemetry consumers into a unified processing fabric. The study aims to characterize the architectural trade-offs associated with telemetry reduction, anomaly preservation, buffering, recovery, and schema compatibility as observability workloads change. The architecture is evaluated through a controlled synthetic simulation rather than production measurements. Ten independent random seeds are executed under normal, anomaly, backpressure, and combined anomaly/backpressure regimes. Fixed-rate, tail-based, and confidence-gated adaptive sampling strategies are compared using telemetry volume reduction and anomaly coverage. At the same time, peak buffering, data loss, and recovery behavior are examined under constrained downstream processing. A separate schema-evolution experiment evaluates tolerance to the addition of telemetry fields without requiring modifications to existing downstream consumers. The simulation results indicate that adaptive sampling can reduce telemetry volume while preserving a substantial proportion of anomalous observations. However, the degree of reduction and preservation varies across sampling strategies and operating conditions. Backpressure scenarios demonstrate how constrained downstream processing affects buffer accumulation, data loss, and recovery behavior, highlighting the importance of explicit streaming control. The schema-evolution experiment indicates that additive changes to telemetry structures can be accommodated without disrupting independent consumers when storage and interface compatibility are decoupled from consumer processing logic. These results provide comparative evidence of architectural behavior under controlled conditions rather than production performance measurements. The study concludes that separating sampling, streaming control, storage compatibility, and consumer processing provides a structured architectural basis for managing telemetry ingestion under variable observability demands while making key trade-offs explicit for subsequent empirical validation]]>
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