Building End-to-End Solution Using Azure Synapse Analytics — Lesson 2: Data Lake, SQL, Spark, and Transformation

Learning Objectives

• Organize lake storage, file formats, partitions, zones, metadata, and lifecycle controls
• Use serverless and dedicated SQL approaches for querying and serving data
• Apply Spark notebooks and transformation patterns for scalable data preparation

Detailed Topics

• Organize lake storage: Organize lake storage, file formats, partitions, zones, metadata, and lifecycle controls
• Use serverless and dedicated SQL approaches for querying and serving data: Use serverless and dedicated SQL approaches for querying and serving data
• Apply Spark notebooks and transformation patterns for scalable data preparation: Apply Spark notebooks and transformation patterns for scalable data preparation

Guided Demonstration

1. Review the requirement, environment, dependencies, roles, risks, and expected outcome.
2. Demonstrate the relevant configuration, analysis, code, or administration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, or test evidence.
4. Compare the observed result with the stated acceptance criteria.

Hands-On Activity

Transform and validate a dataset using lake, SQL, and Spark components.

• Document assumptions and the planned sequence before making changes.
• Complete the activity and preserve evidence of normal and exception behavior.
• Record issues, corrective actions, and reusable operational or development notes.

Troubleshooting Focus

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

Validation Checklist

• The requirement and expected outcome are clear.
• The selected configuration, design, query, code, or process is documented.
• Normal, boundary, failure, security, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Data Lake, SQL, Spark, and Transformation in a realistic scenario and validate the result with documented evidence.