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Advanced Data Engineering for Risk and Regulatory Information Systems: A Comprehensive Study of Data Readiness, Quality Controls, Reference Data, Lineage, Transformation, Reconciliation, and Regulatory Reporting Architectures
ISBN Book

Advanced Data Engineering for Risk and Regulatory Information Systems: A Comprehensive Study of Data Readiness, Quality Controls, Reference Data, Lineage, Transformation, Reconciliation, and Regulatory Reporting Architectures

Author:
Sudha Donepudi

ISBN: 978-93-49929-18-0

DOI: https://doi.org/10.63282/978-93-49929-18-0


Advanced Data Engineering for Risk and Regulatory Information Systems: A Comprehensive Study of Data Readiness, Quality Controls, Reference Data, Lineage, Transformation, Reconciliation, and Regulatory Reporting Architectures provides an in-depth exploration of the data engineering practices essential for reliable risk management and regulatory reporting. The book examines key areas such as data readiness, data quality controls, reference data validation, end-to-end data lineage, data transformation, reconciliation processes, and regulatory reporting architectures. It highlights the importance of data accuracy, consistency, traceability, and governance across complex enterprise data environments, including traditional relational databases and modern Big Data platforms. By addressing data integration, validation frameworks, metadata management, and the development of dependable data pipelines, the book offers valuable insights for data engineers, data analysts, data architects, financial risk professionals, regulatory reporting specialists, and researchers seeking to strengthen enterprise data quality, improve operational efficiency, and build transparent, scalable, and reliable information systems. Designed for data engineers, data architects, financial risk professionals, regulatory reporting specialists, business analysts, technology consultants, and researchers, this book bridges essential technical concepts with the practical demands of enterprise information management. By bringing together the interconnected dimensions of data quality, governance, integration, and reporting architecture, it provides a valuable perspective on building resilient, auditable, and scalable data environments. Ultimately, this work underscores a fundamental principle of modern financial technology: reliable regulatory reporting begins with reliable data engineering.


Sudha Donepudi, Senior Data Analyst with 20 years of IT experience specializing in end-to-end data validation, requirements gathering, planning, and execution across the Financial (BFS), Telecommunications, and Healthcare industries. Proven track record of managing massive data volumes by bridging legacy data warehouses (Oracle, Teradata, DB2, Sybase) with modern Big Data environments (Hadoop, Hive, Impala). Recognized expert in FED regulatory reporting frameworks, retail risk data profiling, and account-level data quality governance. Proficient at translating complex Business Requirement Documents (BRDs) into highly optimized ETL mapping logic, maintaining data lineage, and establishing automated validation strategies using tools like QTP and QlikView.

With over 10 years of experience in Banking and Financial Services (BFS), Sudha Donepudi specializes in data analysis, data validation, and financial data architecture across hybrid environments, including Hadoop, Oracle, and Teradata. Her expertise includes gathering business requirements through Business Requirement Documents (BRDs), implementing data readiness processes, ensuring data quality, validating Federal regulatory reporting data, and maintaining end-to-end data lineage. She has extensive experience in ETL processes using Informatica and Ab Initio, SQL query optimization, UNIX and Shell scripting, and database management across Oracle, DB2, Sybase, and Teradata. Her technical proficiency also encompasses distributed file systems, Hadoop cluster data transfer and replication, high-volume data processing, and data integration to establish a Single Source of Truth (SSOT). Additionally, she is skilled in QlikView reporting, QTP automation, Hive and Impala queries, JIRA-based issue tracking, performance optimization, and enforcing enterprise architecture standards while providing technical guidance to junior engineers and stakeholders.


Title: Advanced Data Engineering for Risk and Regulatory Information Systems: A Comprehensive Study of Data Readiness, Quality Controls, Reference Data, Lineage, Transformation, Reconciliation, and Regulatory Reporting Architectures
Author: Sudha Donepudi
Publisher: ScienceTech Xplore
Copyright: ScienceTech Xplore
Language: English
Publication Format: Online
ISBN: 978-93-49929-18-0
DOI: https://doi.org/10.63282/978-93-49929-18-0
Subject/Genre: AI Architecture
Pages: 1-138
Chapters: 16
Year of Publication: 2026
URL: https://sciencetechxplore.org/978-93-49929-18-0.php
Cite As:

Donepudi, S. (2026). Advanced Data Engineering for Risk and Regulatory Information Systems: A Comprehensive Study of Data Readiness, Quality Controls, Reference Data, Lineage, Transformation, Reconciliation, and Regulatory Reporting Architectures. ScienceTech Xplore. https://doi.org/10.63282/978-93-49929-18-0

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