Data Transformation Using Spark

Course Description

Data Transformation Using Spark develops practical, role-relevant capability in transforming large datasets with Apache Spark, Spark SQL, PySpark, Azure Databricks, Synapse Spark pools, modern data warehouses, and optimized pipelines. The course connects important platform concepts with implementation decisions, operational controls, and realistic workplace scenarios.

Learners progress through four structured lessons aligned to the current source course focus. The learning path emphasizes accurate configuration or development, secure practices, reliable operations, collaboration, and evidence-based troubleshooting.

By completing the course, learners can apply the skills to role-specific tasks, review results against clear acceptance criteria, and create a practical improvement plan for continued development.

What You Will Learn

• Explain drivers, executors, clusters, partitions, lazy evaluation, and distributed processing
• Create Spark sessions and load structured and semi-structured datasets
• Plan transformations, actions, schemas, data quality, and output requirements
• Use DataFrames and Spark SQL for filtering, joining, grouping, aggregation, and windows
• Handle missing values, duplicates, types, dates, nested data, and business rules
• Validate transformation accuracy and detect skew, shuffle, and inefficient operations
• Develop reusable PySpark notebooks and transformation functions
• Use Azure Databricks and Synapse Spark pools for managed analytics workloads

SkillDynamo Training Approach

SkillDynamo delivers this Data Transformation Using Spark course through instructor-led demonstrations, hands-on exercises, scenario analysis, validation checklists, structured troubleshooting, evidence-based reviews, and a final improvement plan linked to practical workplace outcomes.