Current Journal of Applied Science and Technology

Modern Lakehouse platforms provide extensive operational observability, including queryable telemetry, execution histories, and runtime diagnostics. Yet data engineering teams repeatedly diagnose and resolve the same failure patterns with no platform-level memory of prior resolutions. This paper formalizes this disconnect as the Observability–Reliability Gap: a structural property of platforms that can reconstruct any individual failure in detail but cannot recognize a current failure as an instance of a previously resolved class. I introduce Failure Fingerprinting, a technique that encodes each failure episode as a normalized feature vector derived from operational signals at failure time, maps it to a stable SHA-256-based identifier, and stores it in a queryable registry. I propose a four-layer reference architecture spanning signal collection, feature engineering, fingerprint generation, and a failure intelligence layer supporting historical matching, fleet-wide pattern detection, and pre-execution predictive warnings. I describe a five-phase incremental adoption methodology delivering standalone operational value at each phase. I further consider the framework in the context of AI-native workloads—embedding pipelines, vector index builds, and large language model inference batches—where static threshold-based alerting is inadequate. This paper presents a prospective framework contribution; all quantitative projections are grounded in published benchmarks from analogous systems, and a complete empirical evaluation design is specified for future validation on production Lakehouse failure data.