Automated Data Validation Pipelines for Multi-Domain Analytics
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Automated Data Validation Pipelines for Multi-Domain Analytics
Authors:
Santosh Vinnakota
Software Engineer
Tennessee, USA
Abstract—Automated data validation is a critical component in multi-domain analytics, ensuring data accuracy, consistency, and reliability across heterogeneous sources. This paper presents an architectural framework for automated data validation pipelines, discussing best practices, implementation strategies, and key challenges. The proposed framework leverages modern data engineering paradigms, including schema enforcement, rule-based validation, anomaly detection, and data reconciliation. The paper further explores the integration of machine learning techniques for adaptive validation and highlights case studies in healthcare, finance, and logistics domains.
Keywords—Automated Data Validation, Data Quality, Multi-Domain Analytics, Machine Learning, Data Engineering.