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

Introduction, Objectives, and Migration Strategy

• Course objectives, participant profile alignment, and success metrics
• High-level migration methodologies and risk assessment
• Configuration of workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

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

Day 1 Lab — Translating a Stored Procedure

• Practical migration of a sample stored procedure into a notebook format
• Mapping temporary tables and cursors to DataFrame transformations
• Validation and comparison against original output results

Day 2 — Advanced Delta Lake & Incremental Loading

• ACID transactions, commit logs, versioning mechanisms, and time travel features
• Auto Loader, MERGE INTO patterns, upsert operations, and schema evolution
• OPTIMIZE, VACUUM, Z-ORDER, partitioning strategies, and storage tuning

Day 2 Lab — Incremental Ingestion & Optimization

• Implementation of Auto Loader ingestion and MERGE workflows
• Application of OPTIMIZE, Z-ORDER, and VACUUM commands with result validation
• Evaluation of read/write performance enhancements

Day 3 — SQL in Databricks, Performance & Debugging

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

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 data 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 methods
• Converting loops and cursors into vectorized DataFrame operations
• Modular code structure, UDFs/pandas UDFs, widgets, and reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Refactoring 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 with quality rules and schema validation
• Integration with Git (GitHub/Azure DevOps), CI pipelines, and testing strategies for PySpark

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, data 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 outcomes and key learnings
• Gap analysis, recommended follow-up actions, and handover of training materials
• References, further learning pathways, and support options