Master Data Engineering with Azure Synapse and PySpark

Course Description

This SkillDynamo course develops practical, role-relevant capability in building modern data-engineering solutions with Azure Synapse Analytics and PySpark for ingestion, transformation, lakehouse and warehouse integration, orchestration, performance, and security. 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 Synapse workspaces, data lake integration, serverless and dedicated SQL, Spark pools, notebooks, pipelines, and linked services
• Design storage zones, file formats, schemas, partitions, naming, access, lifecycle, and medallion architecture
• Use PySpark DataFrames, functions, joins, windows, aggregations, schemas, errors, and reusable transformation patterns
• Implement ingestion, cleansing, deduplication, enrichment, incremental loads, merge, validation, and quality evidence
• Create Synapse pipelines, triggers, parameters, dependencies, notebooks, copy activities, orchestration, and error handling
• Integrate lakehouse, warehouse, SQL, Spark, Power BI, external data, and semantic consumption patterns
• Tune Spark, SQL, distributions, partitions, caching, file sizes, statistics, concurrency, compute, and cost
• Apply identities, roles, networking, private endpoints, secrets, encryption, monitoring, alerts, recovery, and 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.