Azure Databricks with PySpark

Course Description

This SkillDynamo course develops practical, role-relevant capability in engineering scalable data solutions with Azure Databricks and PySpark using DataFrames, transformations, Delta Lake, streaming, jobs, performance tuning, and governance. 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

• Use Python, Spark sessions, RDD concepts, DataFrames, schemas, expressions, functions, SQL, joins, aggregations, and windows
• Configure Databricks workspaces, clusters, libraries, notebooks, repositories, secrets, storage access, and collaboration
• Read and write files, databases, APIs, cloud storage, and Delta tables with reliable schemas and incremental patterns
• Build transformations for cleansing, validation, deduplication, enrichment, merge, change data capture, and medallion layers
• Develop structured-streaming workloads with triggers, checkpoints, watermarks, state, event-time logic, and fault recovery
• Create jobs, workflows, parameters, dependencies, retries, notifications, schedules, and deployment-ready pipelines
• Optimize shuffles, joins, partitions, caching, skew, file sizes, Delta maintenance, cluster sizing, and cost
• Apply access controls, secrets, lineage, quality checks, monitoring, alerts, incident response, and operational documentation

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.