Neeraj Bhatyal

Books / The Enterprise Data Architect

THE BOOK

References and Further Reading

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The works below informed the perspectives in this book and are recommended for readers who want to go deeper. They are grouped by theme. Standards and regulatory frameworks are listed in their own section; readers should always consult the current official version.

Enterprise Architecture and Architectural Practice

  • Evans, Eric. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston: Addison-Wesley, 2003.
  • Hohpe, Gregor. The Software Architect Elevator: Redefining the Architect’s Role in the Digital Enterprise. Sebastopol, CA: O’Reilly Media, 2020.
  • Nygard, Michael. “Documenting Architecture Decisions.” Cognitect blog, November 15, 2011.
  • Skelton, Matthew, and Manuel Pais. Team Topologies: Organizing Business and Technology Teams for Fast Flow. Portland, OR: IT Revolution Press, 2019.
  • The Open Group. The TOGAF Standard, 10th Edition. The Open Group, 2022.

Data Management, Modeling, and Governance

  • DAMA International. DAMA-DMBOK: Data Management Body of Knowledge. 2nd ed. Basking Ridge, NJ: Technics Publications, 2017.
  • EDM Council. DCAM: Data Management Capability Assessment Model. EDM Council (current edition).
  • Inmon, W. H. Building the Data Warehouse. 4th ed. Indianapolis: Wiley, 2005.
  • Kimball, Ralph, and Margy Ross. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. 3rd ed. Indianapolis: Wiley, 2013.
  • Linstedt, Dan, and Michael Olschimke. Building a Scalable Data Warehouse with Data Vault 2.0. Waltham, MA: Morgan Kaufmann, 2015.

Data Engineering, Platforms, and Cloud

  • Armbrust, Michael, Ali Ghodsi, Reynold Xin, and Matei Zaharia. “Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics.” Proceedings of the Conference on Innovative Data Systems Research (CIDR), 2021.
  • Hohpe, Gregor, and Bobby Woolf. Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Boston: Addison-Wesley, 2003.
  • Kleppmann, Martin. Designing Data-Intensive Applications. Sebastopol, CA: O’Reilly Media, 2017.
  • Reis, Joe, and Matt Housley. Fundamentals of Data Engineering. Sebastopol, CA: O’Reilly Media, 2022.
  • Storment, J. R., and Mike Fuller. Cloud FinOps. 2nd ed. Sebastopol, CA: O’Reilly Media, 2023.
  • Fowler, Martin. “StranglerFigApplication.” martinfowler.com, 2004.

Data Products, Data Mesh, and Data Quality

  • Dehghani, Zhamak. Data Mesh: Delivering Data-Driven Value at Scale. Sebastopol, CA: O’Reilly Media, 2022.
  • Jones, Andrew. Driving Data Quality with Data Contracts. Birmingham: Packt Publishing, 2023.
  • Moses, Barr, Lior Gavish, and Molly Vorwerck. Data Quality Fundamentals. Sebastopol, CA: O’Reilly Media, 2022.

Semantics and Knowledge Graphs

  • Allemang, Dean, James Hendler, and Fabien Gandon. Semantic Web for the Working Ontologist. 3rd ed. ACM Books, 2020.
  • Barrasa, Jesús, and Jim Webber. Building Knowledge Graphs: A Practitioner’s Guide. Sebastopol, CA: O’Reilly Media, 2023.
  • Hogan, Aidan, et al. “Knowledge Graphs.” ACM Computing Surveys 54, no. 4 (2021).

Artificial Intelligence, Generative AI, and Retrieval

  • Gao, Yunfan, et al. “Retrieval-Augmented Generation for Large Language Models: A Survey.” arXiv:2312.10997, 2023.
  • Huyen, Chip. Designing Machine Learning Systems. Sebastopol, CA: O’Reilly Media, 2022.
  • Huyen, Chip. AI Engineering: Building Applications with Foundation Models. Sebastopol, CA: O’Reilly Media, 2025.
  • Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems (NeurIPS) 33, 2020.
  • Sculley, D., et al. “Hidden Technical Debt in Machine Learning Systems.” Advances in Neural Information Processing Systems (NeurIPS) 28, 2015.
  • Vaswani, Ashish, et al. “Attention Is All You Need.” Advances in Neural Information Processing Systems (NeurIPS) 30, 2017.
  • Yao, Shunyu, et al. “ReAct: Synergizing Reasoning and Acting in Language Models.” International Conference on Learning Representations (ICLR), 2023.

Decision Intelligence and Leadership

  • Pratt, Lorien. Link: How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Bingley, UK: Emerald Publishing, 2019.

Standards, Frameworks, and Regulation

  • Basel Committee on Banking Supervision. Principles for Effective Risk Data Aggregation and Risk Reporting (BCBS 239). Bank for International Settlements, 2013.
  • European Union. Regulation (EU) 2016/679 (General Data Protection Regulation).
  • European Union. Regulation (EU) 2024/1689 (Artificial Intelligence Act).
  • International Organization for Standardization. ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management system. ISO/IEC, 2023.
  • National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1, 2023.
  • National Institute of Standards and Technology. NIST Privacy Framework, Version 1.0. 2020.
  • National Institute of Standards and Technology. Zero Trust Architecture. NIST Special Publication 800-207, 2020.
  • OWASP Foundation. OWASP Top 10 for Large Language Model Applications (2025 edition). OWASP, 2024.