Comprehensive Data Engineering with Python and Azure Databricks — Lesson 1: Python, SQL, Spark and Databricks Engineering Foundations

Learning Objectives

• Use Python, SQL, notebooks, Spark sessions, DataFrames, schemas, functions, joins, windows, and reusable engineering patterns
• Configure Databricks workspaces, clusters, compute policies, repositories, libraries, storage access, and development workflows
• Validate python, sql, spark and databricks engineering foundations decisions with documented requirements, evidence, risks, and measurable acceptance criteria

Detailed Topics

• Use Python: Use Python, SQL, notebooks, Spark sessions, DataFrames, schemas, functions, joins, windows, and reusable engineering patterns
• Configure Databricks workspaces: Configure Databricks workspaces, clusters, compute policies, repositories, libraries, storage access, and development workflows
• Validate python: Validate python, sql, spark and databricks engineering foundations 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

Create a requirement and readiness checklist, then design or configure the first working foundation.

• 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 Python, SQL, Spark and Databricks Engineering Foundations in a realistic scenario and validate the result with documented evidence.