A Distributed Data Validation Architecture for Ensuring Accuracy, Consistency, and Traceability Across Hadoop-Based Financial Data Processing Ecosystems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V1I1P104Keywords:
Data Validation, Hadoop Ecosystem, Data Lineage, Financial Data, Data QualityAbstract
Financial institutions process transaction, risk, and ledger data on Hadoop clusters that spread storage and computation across many machines. This distribution multiplies the points where records can be lost, duplicated, altered, or mistook. A distributed data validation architecture addresses these risks through five coordinated layers: ingestion verification, processing reconciliation, storage consistency control, lineage capture, and governance reporting. Each layer attaches declarative rules to the stage where a defect is most likely to arise and writes the outcome to a shared metadata catalog. The architecture produces three principal results. Accuracy improves because rules run beside the data on the same parallel engines that transform it. Consistency improves because control totals and referential rules compare records across storage zones and processing stages. Traceability improves because every output value links back to its source records, transformation steps, and validation verdicts. For practitioners, the architecture gives data engineers a repeatable pattern for building trustworthy pipelines, gives auditors verifiable evidence for regulatory reporting, and gives business owners confidence that figures in financial statements, and risk reports reflect their origins.
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