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Chapter 11 — Data Warehousing and Business Intelligence

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CDMP Fundamentals weight: 10% — major domain
Current source boundary: DAMA-DMBOK2 Revised, Chapter 11, pp. 361–393.

This chapter is not merely about building a database for reports. It is about turning business questions into a governed analytical environment that preserves useful history, integrates inconsistent sources, delivers the right analytical experience to different users, and remains trustworthy after go-live.

Final mental model

Business need → history / grain / latency → source & Data Quality reality → architecture → integration / storage / presentation → BI user need → Metadata / lineage / Governance / Security → production lifecycle → metrics & improvement

When a question jumps directly to a fashionable tool, streaming, a dashboard, or an aggregation before resolving the requirement and control problem, treat that answer as suspicious.

Study route

  1. 01 — Guided Learning — build the mental model in ten lessons.
  2. 02 — Exam Map — memorize the high-yield distinctions and decision rules.
  3. 03 — Visual Memory Atlas — redraw 12 chapter maps from memory.
  4. 04 — Battle Cards — rapid and deep discrimination practice.
  5. 05 — Scenario Lab — apply the chapter to 24 scenarios plus a capstone.
  6. 06 — Question Bank — 68 source-bound exam questions with separate diagnostics.
  7. 07 — Teach-Back — retrieval, reconstruction, classification, and repair.

The exam spine

You should be able to explain and apply these without hesitation: - BI vs Data Warehouse vs Data Warehousing. - Inmon/CIF vs Kimball. - Staging vs ODS vs Data Warehouse vs Data Mart vs Cube. - Fact vs Dimension vs Conformed Dimension. - Atomic detail vs premature summarization. - Historical load vs recurring updates. - Five CDC methods: timestamp, change table, transaction log, message delta, full comparison. - Low-latency accumulation: Trickle = source; Messaging = bus; Streaming = target. - Six activities: Requirements → Architecture → Develop → Populate → BI Portfolio → Maintain Data Products. - Mapping vs remediation vs transformation. - Optimistic vs pessimistic late-arriving-dimension handling. - Operational/tactical vs strategic BI; governed self-service vs unmanaged analysis. - Dictionary/Metadata vs lineage vs impact analysis. - Pilot/sandbox vs production readiness. - Usage vs coverage vs performance vs satisfaction evidence.

Current Drive originals

Artifact 08 remains performance-driven. Do not prefill an error-repair guide from predicted weaknesses; build it only from real misses, timing, confidence, and repeated confusion.

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