Real-Time Operations Analytics Training
As of 2026-08-16
The decision this module trains is blunt: which of three latency tiers — near-real-time (<60s, CDC-based), operational (1–15 min, micro-batch), or analytical (15 min–hourly, scheduled) — a given data flow actually needs. Get it wrong in one direction and Capacity Unit consumption multiplies for no decision-relevant gain; get it wrong in the other and the supply-chain dashboard or Joule assistant runs on data nobody trusts. The concrete test: can you size a streaming pipeline against a stated throughput — 50,000 events/minute from an IIoT source is this module's working case — and hand over a Replication Flow config, a Data Access Control design, and a governance runbook the client's own team can operate without you. Consultants who can do that are the ones clients trust with the Joule and automation build-out, and price accordingly.
What you will learn
- Assign a client data flow to the correct SAP latency tier — near-real-time CDC via Replication Flow, micro-batch via Data Intelligence/BTP, or scheduled Data Flow — and justify the assignment against stated business SLAs
- Size a streaming architecture for a given throughput budget: calculate required Datasphere partitioning, select local versus HANA Cloud table landing, and specify the error-handling path
- Design a Data Access Control schema that separates raw-stream access from aggregated-model access and enforce it without performance regression on a sub-60-second pipeline
- Produce a three-option executive brief recommending a streaming architecture pattern, naming rejected alternatives, quantified cost and latency trade-offs, and a governance runbook
Real-time data feeds Joule, powers event-triggered automation, and keeps supply-chain and manufacturing dashboards honest — but it also multiplies the cost of a wrong architecture decision, because a streaming pipeline built where a scheduled batch would have served the business, or a federated pattern built where replication was actually needed, is expensive to unwind once downstream consumers depend on it. This module gives a senior SAP analytics consultant the decision framework and hands-on pattern for choosing, sizing, and governing real-time and streaming analytics inside an SAP landscape.
Why this is a field decision, not a syllabus item
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C045, C087, C083
Outcomes
- Understand the core concepts behind real-time & streaming analytics
- Apply Streaming in a typical SAP analytics engagement
- Explain the core architecture and decision points for Real-Time & Streaming Analytics
- 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.