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

Introduction, Objectives, and Migration Strategy

• Course objectives, alignment with participant profiles, and success metrics
• High-level migration approaches and associated risk assessments
• Configuration of workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

• Lakehouse concepts, Delta Lake overview, and Databricks architecture
• Implications of SMP vs MPP differences for migration strategies
• Medallion (Bronze→Silver→Gold) design patterns and Unity Catalog overview

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
• Output validation and comparison against the original results

Day 2 — Advanced Delta Lake & Incremental Loading

• ACID transactions, commit logs, versioning, and time travel capabilities
• 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

• Implementation of Auto Loader ingestion and MERGE workflows
• Application of OPTIMIZE, Z-ORDER, and VACUUM; result verification
• Assessment of read/write performance enhancements

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL features: window functions, higher-order functions, and JSON/array manipulation
• Interpreting the Spark UI: DAGs, shuffles, stages, tasks, and bottleneck identification
• Query optimization: broadcast joins, hints, caching, and spill mitigation

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring complex SQL processes into optimized Spark SQL
• Utilizing Spark UI traces to detect and resolve skew and shuffle problems
• Before-and-after benchmarking and documentation of tuning 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, UDFs/pandas UDFs, widgets, and creation of reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Converting procedural ETL scripts into modular PySpark notebooks
• Incorporating parametrization, unit-style tests, and reusable functions
• Code review and application of 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/Azure DevOps), CI, and testing strategies for PySpark logic

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

• Constructing a Bronze→Silver→Gold pipeline orchestrated via Workflows
• Implementation of logging, auditing, retries, and automated validations
• Full pipeline execution, output validation, and preparation of deployment documentation

Operationalization, Governance, and Production Readiness

• Unity Catalog governance, lineage, and access control best practices
• Cost management, cluster sizing, autoscaling, and job concurrency patterns
• Deployment checklists, rollback strategies, and runbook creation

Final Review, Knowledge Transfer, and Next Steps

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