Data Management Engineering

Short Description

Technical practitioners usually know a single tool well — a particular DBMS, an ETL platform, a BI system — but rarely see how those tools fit together into one coherent data management system for the organization. The result is a set of decisions that are locally correct but systemically inconsistent: a data model with no shared glossary of terms behind it, an integration pipeline with no lineage tracking, a quality metric nobody ties back to business consequences. This book supplies exactly the shared vocabulary and the map of knowledge areas that usually goes missing between individual tools.

The book's approach rests on the systematized practices and recommendations of DAMA International, an international professional association, translated into the language of concrete engineering decisions — architecture-selection criteria, model-design checklists, step sequences for rolling out quality controls — rather than a retelling of management meetings or vendor marketing promises. A single running technical example, an online store whose data architecture, models, pipelines, and organizational structure run through the whole book, lets the reader see how decisions made in different chapters add up to one coherent system.

What the Book Covers

The book is organized into 7 parts and 21 chapters.

Part I. Foundations of Data Management (Chapters 1–4) — DAMA concepts and framework, data handling ethics, and data governance: strategy, implementation, tools, standards, and metrics.

Part II. Architecture and Modeling (Chapters 5–7) — data architecture, the entity-relationship approach to modeling, and alternative notations (multidimensional, object-oriented, fact-based, temporal, NoSQL) along with criteria for choosing among them.

Part III. Storage, Security, and Integration (Chapters 8–10) — database storage and operations, data security, and integration and interoperability (ETL/ELT, enterprise service bus, APIs).

Part IV. Content, Reference, and Master Data (Chapters 11–12) — document and content management, electronic discovery, reference and master data, entity resolution.

Part V. Analytics, Metadata, and Quality (Chapters 13–16) — data warehousing and business intelligence, metadata management, and measuring and implementing data quality along with its ties to other knowledge areas.

Part VI. Big Data and Data Science (Chapters 17–18) — strategy, sources, tools, techniques, and governance for working with large volumes of loosely structured data.

Part VII. Maturity, Organization, and Change (Chapters 19–21) — data management maturity assessment, organizational structure and roles, and managing organizational change during the rollout of technical initiatives.

Who This Book Is For

Data engineers, data architects, analysts, database administrators, data stewards, and developers. A basic familiarity with databases and the software development lifecycle is assumed — the book deliberately stays focused on technical implementation rather than the management level, which is the subject of a separate, companion book for executives.

What Sets This Book Apart

• Engineering language that never dilutes the substance. Every chapter gives the technical reader concrete selection criteria — ETL versus ELT, the relational model versus NoSQL, the Inmon architectural path versus Kimball's — rather than a retelling of vendor documentation or general principles detached from an actual decision.
• Consistent terminology from cover to cover. All twenty-one chapters are built on one shared set of concepts, consolidated into a glossary at the end of the book: a term defined once is used consistently afterward, without drift between chapters.
• One running example that grows with the book. The same online store — with the same "Customer," "Product," and "Order" entities — follows the reader from data storage in Chapter 8 through organizational change in Chapter 21, so the technical decisions of different chapters are visible within a single coherent system rather than as disconnected examples pulled from unrelated domains.