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 definition of success metrics
• Overview of high-level migration approaches and associated risk factors
• Configuration of workspaces, repositories, and laboratory datasets

Day 1 — Migration Fundamentals and Architecture

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

Day 1 Lab — Translating a Stored Procedure

• Practical migration of a sample stored procedure to a notebook
• Mapping temporary tables and cursors to DataFrame transformations
• Validation and comparison of outputs against the original implementation

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
• Storage optimization techniques: 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; verifying results
• Evaluating improvements in read/write performance

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL capabilities: window functions, higher-order functions, and JSON/array processing
• Analyzing Spark UI, DAGs, shuffles, stages, and tasks to diagnose bottlenecks
• Query optimization strategies: broadcast joins, hints, caching, and reducing spill

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring a resource-intensive SQL process into optimized Spark SQL
• Utilizing Spark UI traces to detect and resolve skew and shuffle issues
• Conducting before/after benchmarks 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 techniques, UDFs/pandas UDFs, widgets, and reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Refactoring a procedural ETL script into modular PySpark notebooks
• Implementing 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 handling
• 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

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

Operationalization, Governance, and Production Readiness

• Unity Catalog governance, data lineage, and access control best practices
• Managing costs, cluster sizing, autoscaling, and job concurrency patterns
• Creating deployment checklists, rollback strategies, and operational runbooks

Final Review, Knowledge Transfer, and Next Steps

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