Power Automate for Azure Data Engineers — Lesson 1: Azure Data Automation Architecture

Learning Objectives

• Map data-engineering events, triggers, dependencies, users, and expected outcomes
• Select Azure, Microsoft 365, HTTP, database, and storage connector patterns
• Plan environments, identities, permissions, connection references, and DLP boundaries

Detailed Topics

• Map data-engineering events: Map data-engineering events, triggers, dependencies, users, and expected outcomes
• Select Azure: Select Azure, Microsoft 365, HTTP, database, and storage connector patterns
• Plan environments: Plan environments, identities, permissions, connection references, and DLP boundaries

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant configuration, application, automation, analysis, security, or development 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

Design an automation architecture for a multi-stage Azure data workflow.

• 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 Azure Data Automation Architecture in a realistic scenario and validate the result with documented evidence.