DP-203T00: Data Engineering on Microsoft Azure — Lesson 3: Real-Time Analytics and Data Serving

Learning Objectives

• Process streaming data with windows, state, late events, reference data, alerts, and operational outputs
• Design analytical serving layers, warehouses, lakehouses, semantic access, Power BI, and machine-learning integration
• Validate real-time analytics and data serving decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Process streaming data with windows: Process streaming data with windows, state, late events, reference data, alerts, and operational outputs
• Design analytical serving layers: Design analytical serving layers, warehouses, lakehouses, semantic access, Power BI, and machine-learning integration
• Validate real-time analytics and data serving decisions with documented requirements: Validate real-time analytics and data serving decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant design, configuration, development, analysis, administration, or support workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Implement a real-time analytical scenario.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, data, permissions, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, ownership, user impact, and business continuity.

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Real-Time Analytics and Data Serving in a realistic scenario and validate the result with documented evidence.