Databricks Migration Workshop: From Stored Procedures to Lakehouse (5-Day Intensive) Training Course

Introduction, Objectives, and Migration Strategy

• Course goals, alignment with participant profiles, and success criteria
• High-level migration approaches and associated risk considerations
• Setup of workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

• Lakehouse concepts, Delta Lake overview, and Databricks architecture
• Distinctions between SMP and MPP and their impact on migration
• Medallion (Bronze to Silver to Gold) design principles and an overview of Unity Catalog

Day 1 Lab — Translating a Stored Procedure

• Practical migration of a sample stored procedure to a notebook
• Converting temp tables and cursors into DataFrame transformations
• Validation and comparison against the original output

Day 2 — Advanced Delta Lake & Incremental Loading

• ACID transactions, commit logs, versioning, and time travel features
• Auto Loader, MERGE INTO patterns, upserts, and schema evolution
• OPTIMIZE, VACUUM, Z-ORDER, partitioning, and storage tuning techniques

Day 2 Lab — Incremental Ingestion & Optimization

• Implementation of Auto Loader ingestion and MERGE workflows
• Application of OPTIMIZE, Z-ORDER, and VACUUM; verification of results
• Evaluation of read and write performance improvements

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL capabilities: window functions, higher-order functions, JSON and array handling
• Interpreting the Spark UI, DAGs, shuffles, stages, tasks, and identifying bottlenecks
• Query tuning strategies: broadcast joins, hints, caching, and reducing spills

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring a resource-intensive SQL process into optimized Spark SQL
• Leveraging Spark UI traces to identify and resolve skew and shuffle issues
• Benchmarking performance before and after changes and documenting tuning steps

Day 4 — Tactical PySpark: Replacing Procedural Logic

• Spark execution model: driver, executors, lazy evaluation, and partitioning strategies
• Converting loops and cursors into vectorized DataFrame operations
• Modularization, UDFs and pandas UDFs, widgets, and building reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Transforming a procedural ETL script into modular PySpark notebooks
• Incorporating parametrization, unit-style tests, and reusable functions
• Conducting code reviews and applying best-practice checklists

Day 5 — Orchestration, End-to-End Pipeline & Best Practices

• Databricks Workflows: job design, task dependencies, triggers, and error management
• Designing incremental Medallion pipelines with quality rules and schema validation
• Integration with Git (GitHub or Azure DevOps), CI, and testing strategies for PySpark logic

Day 5 Lab — Build a Complete End-to-End Pipeline

• Assembling a Bronze to Silver to Gold pipeline orchestrated via Workflows
• Implementing logging, auditing, retries, and automated validations
• Executing the full pipeline, validating outputs, and preparing deployment documentation

Operationalization, Governance, and Production Readiness

• Best practices for Unity Catalog governance, lineage, and access controls
• Cost management, cluster sizing, autoscaling, and job concurrency patterns
• Deployment checklists, rollback strategies, and creating runbooks

Final Review, Knowledge Transfer, and Next Steps

• Participant presentations on migration work and key takeaways
• Gap analysis, recommendations for follow-up activities, and handover of training materials
• References, further learning paths, and support options