Comprehensive Data Engineering with Python and Azure Databricks

Course Description

This SkillDynamo course develops practical, role-relevant capability in building production-grade data-engineering solutions with Python, SQL, Apache Spark, Azure Databricks, Delta Lake, orchestration, testing, 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, SQL, notebooks, Spark sessions, DataFrames, schemas, functions, joins, windows, and reusable engineering patterns
• Configure Databricks workspaces, clusters, compute policies, repositories, libraries, storage access, and development workflows
• Ingest files, databases, APIs, events, and cloud storage using Auto Loader, connectors, incremental patterns, and checkpoints
• Build Bronze, Silver, and Gold transformations with Delta tables, merge, change data capture, constraints, and schema evolution
• Implement structured streaming, event-time processing, watermarks, deduplication, state, recovery, and reliable sinks
• Orchestrate jobs and pipelines, parameterize workloads, manage dependencies, test transformations, and enforce data quality
• Optimize Spark, partitioning, caching, joins, file sizes, Delta maintenance, cluster sizing, concurrency, and cost
• Apply Unity Catalog, lineage, access controls, secrets, CI/CD, monitoring, 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.