DP-203T00: Data Engineering on Microsoft Azure — Lesson 2: Transformation with Synapse, Spark and Databricks

Learning Objectives

• Use serverless SQL, dedicated SQL, Spark, notebooks, data frames, transformations, and distributed processing
• Apply joins, aggregations, optimization, data cleansing, schema evolution, and reusable processing patterns
• Validate transformation with synapse, spark and databricks decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Use serverless SQL: Use serverless SQL, dedicated SQL, Spark, notebooks, data frames, transformations, and distributed processing
• Apply joins: Apply joins, aggregations, optimization, data cleansing, schema evolution, and reusable processing patterns
• Validate transformation with synapse: Validate transformation with synapse, spark and databricks 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

Develop a scalable transformation pipeline.

• 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 Transformation with Synapse, Spark and Databricks in a realistic scenario and validate the result with documented evidence.