Apache Spark & Big Data Processing Training Course

Course Overview

Learning Objectives

• Analyse Apache Spark architecture and its role in large-scale data processing.
• Understand distributed computing principles and Spark execution models.
• Develop efficient data processing workflows using Spark.
• Work with DataFrames and Spark SQL for large-scale data analysis.
• Apply transformations, actions, filtering, aggregation, and joins to distributed datasets.
• Process structured, semi-structured, and large-scale datasets efficiently.
• Apply batch and real-time data processing concepts using Spark.
• Evaluate partitioning, caching, and resource utilization strategies.
• Identify and resolve common Spark performance and processing issues.
• Apply fault-tolerance and reliability concepts in distributed processing.
• Integrate Spark with modern Big Data storage and data platform environments.
• Develop scalable Spark processing solutions aligned with organizational requirements.

Who Should Attend

Learning Outcomes

• Explain Apache Spark architecture and distributed processing principles.
• Identify the major components of the Spark processing ecosystem.
• Design distributed data processing workflows using Spark.
• Create and manipulate DataFrames for large-scale data analysis.
• Use Spark SQL to query and analyse structured datasets.
• Apply transformations, actions, joins, and aggregations efficiently.
• Process and transform large datasets across distributed environments.
• Develop approaches for batch and streaming data processing.
• Apply partitioning, caching, and optimization techniques.
• Diagnose common processing and performance problems.
• Integrate Spark processing with modern data storage and analytics platforms.
• Develop a scalable Spark-based solution for a selected Big Data use case.

Course Outline