book

DE//AGENT currently lists 3 resources under book: 3 references. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.

Learning objectives are not yet documented for these records — curation adds them evidence-first. What the entries cover today, from their own summaries:

• Organizational principles for analytical data at scale — domain ownership, data as a product, self-serve data platform, and federated computational governance. Zhamak Dehghani's Data Mesh provides the organizational counterpoint to a central platform team; the vocabulary and principles are essential for governance discussions and platform strategy. The four principles (domain ownership, data as product, self-serve platform, federated governance) map directly to our governance and platform patterns. Data Mesh (O'Reilly)
• The canonical book on the principles behind data systems: reliability, scalability, replication, partitioning, consistency, and batch/stream processing. Martin Kleppmann's DDIA is the single highest-value read for system-design interviews and for understanding why data tools are shaped as they are. Every chapter maps directly to patterns in this registry — replication to CDC, partitioning to lakehouse, consistency to transaction semantics. The definitive textbook for data engineers. Designing Data-Intensive Applications (O'Reilly)
• A lifecycle view of data engineering — generation, storage, ingestion, transformation, and serving — with the "undercurrents" of security, governance, and cost. Joe Reis and Matt Housley provide a modern, practitioner-oriented map of the field. The best overview for someone moving into data engineering; pairs perfectly with this platform's roadmap. Covers the full lifecycle from ingestion through serving, with practical patterns for each stage. Fundamentals of Data Engineering (O'Reilly)

These resources map to stages: Reliability (1), System Design (1), Foundations (1). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.

Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.

How scores are computed: source quality score.

Highest composite score first:

1. Data Mesh (O'Reilly) score 26.7 — difficulty not documented
2. Designing Data-Intensive Applications (O'Reilly) score 26.7 — difficulty not documented
3. Fundamentals of Data Engineering (O'Reilly) score 26.7 — difficulty not documented

Scores rank provenance, completeness and trust — read how verification works before adopting anything.

• Data Mesh (O'Reilly) — Organizational principles for analytical data at scale — domain ownership, data as a product, self-serve data platform, and federated computational governance. Zhamak Dehghani's Da… details
• Designing Data-Intensive Applications (O'Reilly) — The canonical book on the principles behind data systems: reliability, scalability, replication, partitioning, consistency, and batch/stream processing. Martin Kleppmann's DDIA is … details
• Fundamentals of Data Engineering (O'Reilly) — A lifecycle view of data engineering — generation, storage, ingestion, transformation, and serving — with the "undercurrents" of security, governance, and cost. Joe Reis and Matt H… details

Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.