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

Introduction, Goals, and Migration Strategy

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

Day 1 — Migration Fundamentals and Architecture

• Core Lakehouse concepts, Delta Lake overview, and Databricks architecture
• Differences 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 — Migrating a Stored Procedure

• Practical migration of a sample stored procedure into a notebook
• Converting temp tables and cursors into DataFrame transformations
• Validating results by comparing them against the original output

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
• Techniques for OPTIMIZE, VACUUM, Z-ORDER, partitioning, and storage optimization

Day 2 Lab — Incremental Ingestion & Optimization

• Building Auto Loader ingestion and MERGE workflows
• Applying OPTIMIZE, Z-ORDER, and VACUUM operations; verifying outcomes
• Assessing improvements in read/write performance

Day 3 — SQL in Databricks, Performance & Debugging

• Advanced analytical SQL features: window functions, higher-order functions, and JSON/array processing
• Interpreting the Spark UI: DAGs, shuffles, stages, tasks, and diagnosing bottlenecks
• Query optimization techniques: broadcast joins, hints, caching, and reducing spill operations

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring complex SQL processes into optimized Spark SQL
• Utilizing Spark UI traces to detect and resolve data skew and shuffle issues
• Benchmarking performance before and after changes and documenting tuning steps

Day 4 — Practical PySpark: Replacing Procedural Logic

• Spark execution model: driver, executors, lazy evaluation, and partitioning strategies
• Replacing loops and cursors with vectorized DataFrame operations
• Implementing modularization, UDFs/pandas UDFs, widgets, and reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Converting procedural ETL scripts 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
• Integrating with Git (GitHub/Azure DevOps), CI pipelines, and testing strategies for PySpark

Day 5 Lab — Building 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, verifying outputs, and preparing deployment documentation

Operationalization, Governance, and Production Readiness

• Best practices for Unity Catalog governance, lineage tracking, and access controls
• 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 on migration work and key takeaways
• Gap analysis, recommendations for follow-up activities, and handover of training materials
• Providing references, further learning paths, and support options