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Academy module

Data Flows & Transformations

Data Flow operator graph: sources through filter pushdown, join, aggregation, to target — architecture diagram for Data Flows & Transformations, Analytics Legends Academy module M006

As of 2026-10-04

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

Data Flow = ETL surface for what Replication Flow can't do (multi-source joins, file ingest, complex derivations, Type 2 SCD). Senior rule: prefer Replication Flow; reach for Data Flow only when transformation is needed. Master 7 operators (Source/Target/Join/Projection/Aggregation/Lookup/Filter + Script). Push filters and projections to source. Header comment on every flow = inheritance protection.

What you will learn

  • Work through a realistic scenario: B2B SaaS, sales-rep performance reporting needs S/4 (orders) + SuccessFactors (rep details) + monthly CSV (territory targets). 4-week build.
  • Recognize and avoid the anti-pattern: Data Flow used for single-source CDC — Higher cost, higher latency than Replication Flow.
  • Apply the module's core decision: Data Flow vs Replication Flow — choose Replication for single SAP source CDC; Data Flow when 2+ sources or transformation needed.
  • Track mastery with the KPI: Run-time P95 (target: < planned window; red flag: > window = pushdown missed or under-CU).

Module overview

Data Flow is the ETL surface in Datasphere — for transformations Replication Flow can't do (joins across sources, complex business logic, external CSV/file ingestion, lookups, derivations). The senior rule: prefer Replication Flow when possible; reach for Data Flow only when transformation is genuinely needed.

When Data Flow wins.

  • Multiple sources (S/4 + SuccessFactors + Salesforce) joined into one fact.
  • File-based sources (CSV uploads, monthly Excel inputs).
  • Complex business derivations (revenue allocation, currency conversion at custom rates, tax derivation).
  • Slowly-changing dimensions (Type 2 history tracking).
  • Pre-aggregation when raw data is too large or too sensitive to expose.

When Data Flow loses.

  • CDC from a single SAP source → use Replication Flow (cheaper, native).
  • Real-time / sub-minute latency → Data Flow runs in batches; latency floor ~minutes.
  • Transformation that should logically live in the analytic model layer → put it in a view, not a Data Flow.

Prerequisites

  • Review core concepts first: C008, C006, C005

Outcomes

  • Work through a realistic scenario: B2B SaaS, sales-rep performance reporting needs S/4 (orders) + SuccessFactors (rep details) + monthly CSV (territory targets). 4-week build.
  • Recognize and avoid the anti-pattern: Data Flow used for single-source CDC — Higher cost, higher latency than Replication Flow.
  • Apply the module's core decision: Data Flow vs Replication Flow — choose Replication for single SAP source CDC; Data Flow when 2+ sources or transformation needed.
  • Track mastery with the KPI: Run-time P95 (target: < planned window; red flag: > window = pushdown missed or under-CU).

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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