Chapter 14 — Big Data and Data Science
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DAMA-DMBOK2 Revised source boundary: printed pp. 471–501
Fundamentals exam weight: 2%
Chapter 14 is a compact exam domain, but its current Mastery Lab package is deliberately decision-oriented. The chapter is not mainly asking you to memorize algorithms. It asks whether you can follow a Big Data/Data Science initiative from business need to source choice, managed ingestion, analytical modeling, communication, deployment, and monitoring without losing control of Metadata, Data Quality, security, governance, or economic value.
Core mental model
BUSINESS NEED → SOURCE CHOICE → INGEST + METADATA → DQ / ALIGNMENT → HYPOTHESIS / MODEL → TRAIN / EVALUATE → COMMUNICATE → DEPLOY / MONITOR → NEW QUESTIONS
When two answers both sound technical, prefer the one that respects this sequence and the business purpose.
Study layers
- Guided Learning — plain-language teaching and chapter logic
- Exam Map — 24 high-yield decision targets
- Visual Memory Atlas — 13 redraw-ready maps
- Battle Cards — 30 rapid + 12 deep comparisons
- Scenario Lab — 20 scenarios + integrated capstone
- Question Bank — 48 balanced exam-style questions with separate diagnostics
- Teach-Back — 60 recall prompts, 12 reconstructions, 20 classification drills
Efficient 2% exam route
If time is limited, master these first: - Big Data vs Data Science; - descriptive vs predictive vs prescriptive; - the six V's as problem clues; - ETL vs ELT; - lake vs swamp; - batch vs speed vs serving; - supervised vs unsupervised vs reinforcement; - source selection → ingest + Metadata → DQ → alignment; - training vs validation vs test and over-fitting; - MPP vs distributed file storage; - business value/latency/cost before production; - recombination privacy risk; - deploy and monitor, not deploy-and-forget.
Source discipline
This GitHub chapter is freshly rebuilt from the current Google Drive Chapter 14 artifacts. It uses DMBOK Chapter 14 terminology and page anchors rather than silently adding newer AI frameworks, vendor features, or terminology.
Artifact 08 remains empty. Only repeated real learner misses should become future error-repair material.