We present an adaptive scheduler for a single differencing engine (SmartDiff) with two execution modes: (i) in-memory threads and (ii) Dask based parallelism. The scheduler continuously tunes batch size and worker/thread count within fixed CPU and memory budgets to minimize p95 latency. A lightweight preflight profiler estimates bytes/row and I/O rate; an online cost/memory model prunes unsafe actions; and a guarded hill-climb policy favors lower latency with backpressure and straggler mitigation. Backend selection is gated by a conservative working-set estimate so that in-memory execution is chosen when safe, otherwise Dask is used. Across synthetic and public tabular benchmarks, the scheduler reduces p95 latency by 23 to 28 percent versus a tuned warm-up heuristic (and by 35 to 40 percent versus fixed grid baselines), while lowering peak memory by 16 to 22 percent (25 to 32 percent vs. fixed) with zero OOMs and comparable throughput.

Data engineering workflows require reliable differencing across files, databases, and query outputs, yet existing tools falter under schema drift, heterogeneous types, and limited explainability. SmartDiff is a unified system that combines schema-aware mapping, type-specific comparators, and parallel execution. It aligns evolving schemas, compares structured and semi-structured data (strings, numbers, dates, JSON/XML), and clusters results with labels that explain how and why differences occur. On multi-million-row datasets, SmartDiff achieves over 95 percent precision and recall, runs 30 to 40 percent faster, and uses 30 to 50 percent less memory than baselines; in user studies, it reduces root-cause analysis time from 10 hours to 12 minutes. An LLM-assisted labeling pipeline produces deterministic, schema-valid multilabel explanations using retrieval augmentation and constrained decoding; ablations show further gains in label accuracy and time to diagnosis over rules-only baselines. These results indicate SmartDiff's utility for migration validation, regression testing, compliance auditing, and continuous data quality monitoring. Index Terms: data differencing, schema evolution, data quality, parallel processing, clustering, explainable validation, big data