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

Introduction, Objectives, and Migration Strategy

• Course goals, alignment with participant profiles, and success criteria.
• Overview of high-level migration approaches and associated risk considerations.
• Setting up workspaces, repositories, and lab datasets.

Day 1 — Migration Fundamentals and Architecture

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

Day 1 Lab — Translating a Stored Procedure

• Hands-on migration of a sample stored procedure into a notebook.
• Mapping temporary tables and cursors to DataFrame transformations.
• Validation and comparison against 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.
• OPTIMIZE, VACUUM, Z-ORDER, partitioning, and storage tuning techniques.

Day 2 Lab — Incremental Ingestion & Optimization

• Implementing Auto Loader ingestion and MERGE workflows.
• Applying OPTIMIZE, Z-ORDER, and VACUUM; validating results.
• Measuring improvements in read/write performance.

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL features: window functions, higher-order functions, and JSON/array handling.
• Reading the Spark UI, DAGs, shuffles, stages, tasks, and diagnosing bottlenecks.
• Query tuning patterns: broadcast joins, hints, caching, and spill reduction.

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring a complex SQL process into optimized Spark SQL.
• Using Spark UI traces to identify and resolve skew and shuffle issues.
• Benchmarking before/after results and documenting 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 creating reusable libraries.

Day 4 Lab — Refactoring Procedural Scripts

• Refactoring a procedural ETL script into modular PySpark notebooks.
• Introducing parametrization, unit-style tests, 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 with Workflows.
• Implementing logging, auditing, retries, and automated validations.
• Running the full pipeline, validating outputs, and preparing deployment notes.

Operationalization, Governance, and Production Readiness

• Best practices for Unity Catalog governance, lineage, and access controls.
• 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 of migration work and lessons learned.
• Gap analysis, recommended follow-up activities, and training materials handoff.
• References, further learning paths, and support options.