Workload-Aware Adaptive ELT Optimization for Reliable Enterprise Data Integration in BigQuery
DOI:
https://doi.org/10.29284/m2h6zz66Keywords:
ELT optimization; enterprise data integration; workload adaptation; schema evolution; BigQuery; data quality; XGBoost; cloud data engineering.Abstract
Enterprise analytics pipelines are commonly built on assumptions that do not hold for long: arrival rates will remain predictable, schemas will stay fixed, and transformation paths will change only when an engineer intervenes. In practice, sales, supply-chain, activation, and telemetry streams evolve independently and arrive at very different rates. This paper develops a workload-aware adaptive ELT framework for high-volume enterprise integration in BigQuery. It combines deterministic controls for schema and data-quality events with machine-learning-assisted selection of workload-specific configurations. A composite ELT Optimization Score (EOS) brings performance, cost efficiency, reliability, and data quality into a single decision measure. The framework was compared with Traditional ETL, Static ELT, Rule-Based Adaptive ELT, and ML-Only Adaptive ELT. Tests covered four heterogeneous source families, workloads ranging from 10 GB to 1 TB, controlled schema mutations, injected quality anomalies, and repeated runs. Compared with Static ELT, the proposed Hybrid Rule + ML design reduced mean latency by 39.4% and normalized processing cost by 31.9%; the latency reduction relative to Traditional ETL was 54.0%. It achieved 99.1% pipeline success, 96.8% schema adaptation, a Data Quality Index of 98.4/100, and an EOS of 94.1/100. At 1 TB, its latency advantage over Static ELT reached 49.7%. It also sustained 99.0% availability and recovered in 3.7 minutes during a combined stress test involving a fivefold workload surge, schema drift, increased duplication, and late-arriving records. These findings indicate that adaptive ELT is valuable not because it produces one universally faster query plan, but because it protects cost, reliability, and data quality as operating conditions become less predictable.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
