Authoritative Data Stores for Treasury Deposits: Sourcing, Certification, and Provisioning Standards for Enterprise Downstreams

The rapid growth of enterprise data volumes and the increasing demand for timely, accurate, and auditable financial reporting have compelled large organizations to rethink traditional extract–transform–load (ETL) architectures supporting treasury deposit systems. Between 2018 and 2019, enterprises—particularly in banking and financial services—faced mounting limitations with legacy SAS, mainframe COBOL, Informatica, and Oracle PL/SQL-based batch processing platforms. These systems were costly, vertically scaled, and constrained by overnight batch windows that could no longer meet modern regulatory, operational, and analytical requirements. This paper presents a comprehensive architectural and methodological framework for building Authoritative Data Stores (ADS) for treasury deposits using Apache Spark as the core distributed processing engine. The framework addresses data sourcing, certification, governance, and standardized provisioning to downstream consumers such as regulatory reporting, risk analytics, finance, and real-time dashboards. It elaborates on enterprise migration drivers, architectural comparisons between legacy ETL systems and Spark-based ecosystems, and the key challenges encountered during large-scale modernization initiatives. A structured migration methodology is proposed, encompassing baseline legacy assessment, code translation, data quality reconciliation, performance optimization, orchestration redesign, and phased cutover strategies. The paper further illustrates best practices through representative case studies demonstrating significant performance gains, cost reduction, and improved scalability. The findings highlight Spark’s role as a unifying compute platform capable of supporting batch, streaming, and advanced analytics workloads while maintaining the integrity and auditability required for treasury deposit data. The study concludes that Spark-enabled Authoritative Data Stores provide a resilient foundation for enterprise financial data modernization, enabling near real-time insights, improved governance, and long-term adaptability in evolving regulatory and business environments.