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

Introduction, Objectives, and Migration Strategy

• Defining course goals, aligning participant profiles, and establishing success criteria
• Reviewing high-level migration approaches and associated risk factors
• Configuring workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

• Exploring Lakehouse concepts, Delta Lake overview, and Databricks architecture
• Understanding SMP vs MPP differences and their implications for migration
• Designing the Medallion (Bronze→Silver→Gold) architecture and introducing Unity Catalog

Day 1 Lab — Translating a Stored Procedure

• Practically migrating a sample stored procedure to a notebook
• Mapping temp tables and cursors to DataFrame transformations
• Validating and comparing results with the original output

Day 2 — Advanced Delta Lake & Incremental Loading

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

Day 2 Lab — Incremental Ingestion & Optimization

• Setting up Auto Loader ingestion and MERGE workflows
• Executing OPTIMIZE, Z-ORDER, and VACUUM operations and validating outcomes
• Assessing improvements in read/write performance

Day 3 — SQL in Databricks, Performance & Debugging

• Utilizing analytical SQL features: window functions, higher-order functions, and JSON/array handling
• Interpreting the Spark UI, DAGs, shuffles, stages, tasks, and diagnosing bottlenecks
• Applying query tuning patterns: broadcast joins, hints, caching, and spill reduction

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring complex SQL processes into optimized Spark SQL
• Leveraging Spark UI traces to identify and resolve skew and shuffle issues
• Benchmarking before and after changes and documenting tuning procedures

Day 4 — Tactical PySpark: Replacing Procedural Logic

• Understanding the Spark execution model: driver, executors, lazy evaluation, and partitioning strategies
• Converting loops and cursors into vectorized DataFrame operations
• Implementing modularization, UDFs/pandas UDFs, widgets, and reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Converting 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

• Designing Databricks Workflows: job structure, task dependencies, triggers, and error handling
• Building incremental Medallion pipelines with quality rules and schema validation
• Integrating with Git (GitHub/Azure DevOps), CI, and testing strategies for PySpark logic

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

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

Operationalization, Governance, and Production Readiness

• Applying Unity Catalog governance, lineage, and access control best practices
• Managing cost, cluster sizing, autoscaling, and job concurrency patterns
• Preparing deployment checklists, rollback strategies, and runbooks

Final Review, Knowledge Transfer, and Next Steps

• Presenting participant migration work and key lessons learned
• Conducting gap analysis, recommending follow-up activities, and handing over training materials
• Providing references, further learning paths, and support options