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Academy module

CDC & Change Data Capture

CDC and Change Data Capture: source flows through a capture mechanism into an idempotent merge, then to consumption; three capture mechanisms are compared — architecture diagram for CDC & Change Data Capture, Analytics Legends Academy module M121

As of 2026-08-16

Edited by , sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

CDC & Change Data Capture decides whether a finance or logistics dashboard refreshes every 15 minutes or once a day — and whether the extraction load ever threatens HANA's own transactional workload. On a 900-million-row ACDOCA table, a full nightly load takes 4.5 hours; the same delta captured via SLT or a Datasphere Replication Flow lands in seconds and merges in minutes. The module gives the architect a defensible way to choose between SLT, Replication Flows, and batch, to keep the target merge idempotent and correctly ordered under at-least-once delivery, and to catch drift with a same-day reconciliation control — the judgment a client steering committee expects before signing off a BW/S4 modernisation cutover.

What you will learn

  • Select the right SAP CDC mechanism (SLT, Datasphere Replication Flow, or batch) for a given source table and latency SLA.
  • Design an idempotent, correctly ordered delta-merge pattern that survives at-least-once delivery and propagates hard deletes.
  • Recognize the five named CDC failure modes — log bloat, schema drift, initial-load gaps, ODP mode confusion, currency/unit mishandling — before they reach production.
  • Defend the CDC-vs-batch decision and its reconciliation control in front of a client steering committee.

CDC & Change Data Capture in the SAP Analytics Landscape

Change Data Capture (CDC) is the discipline of identifying, capturing, and delivering only the rows that changed since the last extraction — inserts, updates, and deletes — rather than re-reading an entire table on every cycle. In large SAP environments this distinction is the difference between a five-minute latency data pipeline and a six-hour batch window that consumes HANA memory, network bandwidth, and ABAP workload simultaneously. Every senior architect must be able to select the right CDC mechanism for a given SAP source, integrate it into a modern lakehouse or Datasphere landing zone, and defend that choice against the pressure to 'just do a full load every night.'

Why CDC Matters in SAP Contexts

Prerequisites

  • Intermediate hands-on experience on SAP analytics projects
  • Review core concepts first: C005, C087, C083

Outcomes

  • Understand the core concepts behind cdc & change data capture
  • Apply CDC in a typical SAP analytics engagement
  • Explain the core architecture and decision points for CDC & Change Data Capture
  • Apply a repeatable implementation pattern in a 15-minute lab format

Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.

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