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02 — Exam Map & High-Yield Targets

Chapter weight: 6%. Expect terminology discrimination, architecture-pattern recognition, lifecycle sequencing and scenario choices rather than deep implementation coding.

Must know cold

  • Integration: movement/consolidation of data.
  • Interoperability: systems’ ability to communicate.
  • ETL / ELT: transform before load vs load raw then transform in target.
  • Latency: time between source generation/change and target availability.
  • CDC: detect/transmit only changes; data-based or log-based.
  • Interaction: point-to-point, hub-and-spoke, publish-subscribe.
  • Coupling: tight/synchronous dependency vs loose/asynchronous independence.
  • Canonical model: common exchange format that reduces pairwise transformations.
  • Lifecycle: Plan & Analyze → Design → Develop → Implement & Monitor.
  • Governance: business owns meaning; Metadata/lineage preserve trust.

Must distinguish

Pair / family Deciding clue
ETL vs ELT Where transformation occurs relative to load
Snapshot vs Delta Point-in-time set vs changes since prior transfer
CDC vs Replication Detect/pass changes vs keep synchronized copies
Batch vs Micro-batch Scheduled larger sets vs same model at higher frequency
Async vs Sync Sender continues vs waits for confirmation
Point-to-point vs Hub Direct pairwise vs exchange through common hub
Mapping vs Orchestration What becomes what vs what runs when/depends on what
Discovery vs Profiling Find candidate sources vs inspect actual contents/fitness
Lineage vs Mapping Where data came/went/changed vs source-target conversion rule
Federation vs Virtualization Combined access vs one logical DB/view over heterogeneous stores
EAI / ESB / SOA / CEP App-interface integration / message bus / reusable services / event-pattern intelligence
Migration vs Integration Controlled conversion for app change vs recurring exchange
Virtualization vs Warehouse Virtual access vs persisted integrated history
Sharing Agreement vs Interface Spec Responsibilities/use/service obligations vs technical structure

Must apply

  • Use batch when scheduled high-volume movement satisfies latency.
  • Use micro-batch/CDC/event when freshness requires it.
  • Prefer async/loose when a producer must continue through receiver outage.
  • Accept sync/tight when the transaction truly cannot complete without immediate downstream confirmation.
  • Move toward hub/canonical/reusable services when interface proliferation and semantic inconsistency are the problem.
  • Profile actual source/target data before design assumptions become production defects.
  • Treat mapping and transformation-rule changes as business-governed Metadata.
  • Establish Data Sharing Agreements/MOUs for governed cross-boundary exchange.
  • Choose virtualization/federation when unified access is needed without mandatory physical consolidation.
  • Choose a physical hub/warehouse when persisted history, source-independent availability or predictable performance require it.

Selective memorization

  • Four business-rule categories: definitions, facts, constraints/action assertions, derivations.
  • Five Plan & Analyze activities: requirements, discovery, lineage, profiling, business rules.
  • Five Develop activities: data services, data flows, migration, publication, CEP.
  • Seven tool categories: ETL/transformation, virtualization server, ESB, rules engine, data/process modeling, profiling, Metadata repository.
  • Metric families: availability; volumes/speed; cost/complexity.

60-second route

  1. ETL vs ELT.
  2. Snapshot vs delta vs CDC vs replication.
  3. Batch vs micro-batch vs event/streaming.
  4. Async vs sync.
  5. Point-to-point vs hub vs pub-sub.
  6. Mapping vs orchestration.
  7. Discovery vs profiling vs lineage.
  8. EAI vs ESB vs SOA vs CEP.
  9. Federation/virtualization vs physical persistence.
  10. Business ownership of transformations + sharing agreement + Metadata/lineage.

Changed-fact drills

  1. ETL → ELT: transform before target load; change fact to raw landing first with target-side transformation.
  2. Snapshot → Delta/CDC → Replication: all current rows; change to only inserts/updates/deletes; change again to “keep the same dataset synchronized elsewhere.”
  3. Batch → Micro-batch → Event-driven: overnight deadline; change to five-minute freshness; change again to immediate action as events arrive.
  4. Async → Sync: producer must continue through downstream outage; change to transaction cannot complete without immediate confirmation.
  5. Point-to-point → Hub/canonical: two simple systems; change to many systems with inconsistent pairwise mappings.
  6. Mapping → Orchestration: STATUS 1 → ACTIVE; change to “Customer must finish before Order.”
  7. Discovery → Profiling → Lineage: locate candidate fields; change to actual null/pattern statistics; change again to origin and downstream path.
  8. ESB → CEP: route messages; change to interpret multiple streams and trigger fraud action.
  9. Virtualization → Physical hub/warehouse: unified view without copying; change to persisted history/source independence/performance.
  10. Sharing Agreement → Technical spec: permitted use/responsibility/SLA; change to fields, mappings, endpoint/protocol.

Readiness rule: recognition is not mastery. Explain the fact that changes the architecture answer.

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