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

Introduction, Objectives, and Migration Strategy

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

Day 1 — Migration Fundamentals and Architecture

• Understanding Lakehouse concepts, Delta Lake overview, and Databricks architecture
• Analyzing SMP vs MPP differences and their impact on migration
• Designing the Medallion (Bronze→Silver→Gold) structure and understanding Unity Catalog

Day 1 Lab — Translating a Stored Procedure

• Hands-on migration of a sample stored procedure to a notebook
• Mapping temporary tables and cursors to DataFrame transformations
• Validating and comparing outputs with the original procedure

Day 2 — Advanced Delta Lake & Incremental Loading

• Exploring ACID transactions, commit logs, versioning, and time travel
• Utilizing Auto Loader, MERGE INTO patterns, upserts, and schema evolution
• Optimizing storage through OPTIMIZE, VACUUM, Z-ORDER, and partitioning

Day 2 Lab — Incremental Ingestion & Optimization

• Implementing Auto Loader ingestion and MERGE workflows
• Applying OPTIMIZE, Z-ORDER, and VACUUM while validating results
• Measuring improvements in read/write performance

Day 3 — SQL in Databricks, Performance & Debugging

• Leveraging analytical SQL features: window functions, higher-order functions, and JSON/array handling
• Interpreting Spark UI, DAGs, shuffles, stages, 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
• Using Spark UI traces to identify and resolve skew and shuffle issues
• Benchmarking performance before and after tuning and documenting steps

Day 4 — Tactical PySpark: Replacing Procedural Logic

• Understanding the Spark execution model: driver, executors, lazy evaluation, and partitioning
• Transforming 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
• Introducing 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 logic, task dependencies, triggers, and error handling
• Architecting 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

• Best practices for Unity Catalog governance, lineage, and access controls
• Managing costs, cluster sizing, autoscaling, and job concurrency
• Creating deployment checklists, rollback strategies, and operational runbooks

Final Review, Knowledge Transfer, and Next Steps

• Participant presentations of migration work and key learnings
• Gap analysis, recommended follow-up activities, and handover of training materials
• Providing references, further learning paths, and support options