Azure Databricks with PySpark — Lesson 2: Batch ETL, Data Transformation and Delta Lake

Learning Objectives

• Read and write files, databases, APIs, cloud storage, and Delta tables with reliable schemas and incremental patterns
• Build transformations for cleansing, validation, deduplication, enrichment, merge, change data capture, and medallion layers
• Validate batch etl, data transformation and delta lake decisions with documented requirements, evidence, risks, and measurable acceptance criteria

Detailed Topics

• Read and write files: Read and write files, databases, APIs, cloud storage, and Delta tables with reliable schemas and incremental patterns
• Build transformations for cleansing: Build transformations for cleansing, validation, deduplication, enrichment, merge, change data capture, and medallion layers
• Validate batch etl: Validate batch etl, data transformation and delta lake decisions with documented requirements, evidence, risks, and measurable acceptance criteria

Guided Demonstration

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

Complete a guided implementation scenario and preserve evidence of settings, outputs, and exceptions.

• 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 Batch ETL, Data Transformation and Delta Lake in a realistic scenario and validate the result with documented evidence.