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
• Overview of high-level migration approaches and associated risk factors
• Configuring workspaces, repositories, and laboratory datasets

Day 1 — Migration Fundamentals and Architecture

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

Day 1 Lab — Translating a Stored Procedure

• Practical migration of a sample stored procedure into a notebook
• Mapping temporary tables and cursors to DataFrame transformations
• Validating results and comparing them against the original output

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

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

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL features: window functions, higher-order functions, and JSON/array processing
• Interpreting Spark UI, DAGs, shuffles, stages, tasks, and diagnosing bottlenecks
• Query optimization patterns: broadcast joins, hints, caching, and minimizing spills

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring a resource-intensive SQL process into optimized Spark SQL
• Leveraging Spark UI traces to pinpoint and resolve skew and shuffle issues
• Benchmarking before/after metrics and documenting tuning procedures

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/pandas UDFs, widgets, and building reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Rebuilding a procedural ETL script into modular PySpark notebooks
• Incorporating parametrization, unit-style testing, 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 incorporating 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

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

Operationalization, Governance, and Production Readiness

• Unity Catalog governance, lineage tracking, and access control best practices
• 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 showcasing migration work and key learnings
• Gap analysis, suggested follow-up actions, and handover of training materials
• Reference materials, further learning paths, and support options