Azure Databricks for R-Based Data Analysis & Engineering

Course Description

This SkillDynamo course develops practical, role-relevant capability in using R with Azure Databricks and Apache Spark for scalable data preparation, analysis, modeling, Delta Lake engineering, and governed collaboration. The learning path progresses from essential concepts and planning to implementation, validation, troubleshooting, and operational use.

Learners work through realistic scenarios, guided demonstrations, applied exercises, and evidence-based reviews. Every topic is connected to workplace responsibilities, common risks, quality controls, and measurable outcomes.

The course helps professionals make informed decisions, identify failure points, communicate results, and create an actionable improvement plan for future projects.

What You Will Learn

• Explain Databricks workspaces, clusters, notebooks, Spark architecture, R integration, storage access, and collaborative workflows
• Configure compute, libraries, repositories, secrets, data connections, and reproducible R environments
• Use Spark DataFrames and R interfaces for reading, cleaning, transforming, joining, aggregating, and exploring large datasets
• Apply schemas, missing-value handling, feature preparation, validation, sampling, and distributed-analysis patterns
• Create Delta tables, medallion layers, incremental loads, merges, quality checks, versioning, and reliable pipeline steps
• Parameterize notebooks, schedule jobs, manage dependencies, capture logs, recover from failures, and document lineage
• Build and evaluate analytical or machine-learning workflows with suitable metrics, experiment evidence, and explainable results
• Optimize compute, partitions, caching, file sizes, security, access, costs, monitoring, and operational support

SkillDynamo Training Approach

SkillDynamo delivers this course through demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, solution reviews, and a final improvement plan. Learners repeatedly practice planning, implementation, evidence collection, testing, correction, and communication so the skills transfer confidently into real workplace situations.