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

Introduction, Objectives, and Strategic Migration Framework

• Defining instructional goals, aligning with target audience competencies, and establishing measurable performance indicators
• Evaluating high-level migration methodologies and associated operational risk factors
• Configuration of development environments, version control repositories, and standardized test datasets

Day 1 — Core Migration Principles and System Architecture

• Foundational principles of the Lakehouse model, Delta Lake capabilities, and the Databricks platform structure
• Comparative analysis of Symmetric Multiprocessing (SMP) versus Massively Parallel Processing (MPP) architectures and their impact on migration strategy
• Implementation of the Medallion architecture (Bronze, Silver, Gold layers) and integration with Unity Catalog

Day 1 Practical Application — Conversion of Stored Procedures

• Direct translation of legacy stored procedure logic into executable notebook workflows
• Conversion of temporary tables and cursor-based logic into stateless DataFrame transformations
• Verification of functional integrity through comparative output analysis

Day 2 — Advanced Delta Lake Mechanics and Incremental Data Loading

• Management of ACID transactions, commit history, data versioning, and time travel capabilities
• Application of Auto Loader, MERGE INTO statements, upsert operations, and dynamic schema evolution
• Optimization of storage efficiency and query performance via OPTIMIZE, VACUUM, Z-ORDER, and partitioning strategies

Day 2 Practical Application — Incremental Ingestion and Performance Tuning

• Deployment of Auto Loader pipelines and MERGE-based synchronization workflows
• Execution of storage optimization tasks (OPTIMIZE, Z-ORDER, VACUUM) and validation of data integrity
• Quantification of performance gains in data read and write operations

Day 3 — Advanced SQL Execution in Databricks, Performance Analysis, and Diagnostics

• Utilization of advanced analytical SQL features, including window functions, higher-order functions, and complex JSON/array processing
• Interpretation of Spark UI metrics, Directed Acyclic Graphs (DAGs), shuffle operations, and stage-level bottleneck identification
• Application of query optimization techniques such as broadcast joins, execution hints, result caching, and memory spill mitigation

Day 3 Practical Application — SQL Refactoring and Performance Optimization

• Restructuring complex SQL workloads into optimized Spark SQL equivalents
• Leveraging Spark UI diagnostics to resolve data skew and inefficient shuffle operations
• Conducting baseline versus post-optimization benchmarking and documenting technical tuning procedures

Day 4 — Applied PySpark: Replacement of Procedural Logic

• Comprehension of the Spark execution model, including driver nodes, executors, lazy evaluation, and data partitioning strategies
• Refactoring iterative loops and cursor-based processes into vectorized, distributed DataFrame operations
• Implementation of modular design patterns, user-defined functions (UDFs), pandas UDFs, interactive widgets, and reusable component libraries

Day 4 Practical Application — Modernization of Procedural Scripts

• Re-engineering legacy procedural ETL scripts into structured PySpark notebook modules
• Incorporation of parameterization, unit testing frameworks, and standardized function libraries
• Execution of peer code reviews and application of industry-standard best practice checklists

Day 5 — Orchestration, End-to-End Pipeline Design, and Governance Standards

• Construction of Databricks Workflows, including task dependency mapping, trigger configuration, and robust error handling mechanisms
• Design of incremental Medallion pipelines incorporating data quality rules and schema validation controls
• Integration with Git-based version control (GitHub/Azure DevOps), continuous integration (CI) pipelines, and automated testing protocols for PySpark logic

Day 5 Practical Application — Construction of a Comprehensive End-to-End Pipeline

• Assembly of a complete Bronze-to-Silver-to-Gold pipeline orchestrated via Databricks Workflows
• Implementation of centralized logging, audit trails, automated retry mechanisms, and data validation checks
• Execution of full pipeline simulation, output verification, and preparation of deployment documentation

Operationalization, Governance, and Production Deployment Readiness

• Enforcement of Unity Catalog governance, data lineage tracking, and granular access control policies
• Optimization of resource costs, cluster sizing, autoscaling configurations, and concurrent job management
• Development of deployment checklists, rollback procedures, and operational runbooks

Final Review, Knowledge Consolidation, and Strategic Next Steps

• Participant presentations detailing migration outcomes and critical lessons learned
• Conduct of gap analysis, formulation of recommended follow-up initiatives, and transfer of comprehensive training materials
• Provision of technical references, advanced learning pathways, and ongoing support resources