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

Introduction, Learning Objectives, and Migration Strategy

• Course objectives, alignment with participant profiles, and key success metrics
• Overview of high-level migration approaches and associated risk factors
• Configuration of workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

• Core Lakehouse concepts, Delta Lake introduction, and Databricks architecture
• Distinguishing SMP vs MPP models and their impact on migration strategies
• Medallion (Bronze→Silver→Gold) architecture design and Unity Catalog overview

Day 1 Lab — Translating a Stored Procedure

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

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
• Optimization techniques: OPTIMIZE, VACUUM, Z-ORDER, partitioning, and storage tuning

Day 2 Lab — Incremental Ingestion & Optimization

• Implementing Auto Loader ingestion and MERGE workflows
• Applying OPTIMIZE, Z-ORDER, and VACUUM commands; verifying results
• Assessing read/write performance enhancements

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL capabilities: window functions, higher-order functions, and JSON/array handling
• Interpreting Spark UI: DAGs, shuffles, stages, tasks, and bottleneck identification
• Query tuning strategies: broadcast joins, hints, caching, and reducing spill

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring complex SQL processes into optimized Spark SQL
• Using Spark UI traces to diagnose and resolve skew and shuffle issues
• Benchmarking pre- and post-optimization performance and documenting steps

Day 4 — Tactical PySpark: Replacing Procedural Logic

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

Day 4 Lab — Refactoring Procedural Scripts

• Converting procedural ETL scripts 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 handling
• Designing 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 — Building a Complete End-to-End Pipeline

• Assembling a Bronze→Silver→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
• Creating deployment checklists, rollback strategies, and operational runbooks

Final Review, Knowledge Transfer, and Next Steps

• Participant presentations on migration work and key takeaways
• Gap analysis, recommended follow-up actions, and handover of training materials
• References, advanced learning paths, and support options