Apache Spark: Apply & Evaluate Big Data Workflows
By External Provider
About this course
This course introduces beginners to the foundational and intermediate concepts of distributed data processing using Apache Spark, one of the most powerful engines for large-scale analytics. Through two progressively structured modules, learners will identify Spark’s architecture, describe its core components, and demonstrate key programming constructs such as Resilient Distributed Datasets (RDDs). In Module 1, learners will recognize the principles behind Spark’s distributed computing model and illustrate basic RDD transformations. In Module 2, they will apply advanced transformation logic, implement persistence strategies, and differentiate between file formats like CSV, JSON, Parquet, and Avro for efficient data handling. By the end of the course, learners will be able to analyze Spark applications for optimization, evaluate storage strategies, and develop scalable data processing workflows using core Spark APIs. The course blends conceptual clarity with hands-on examples to equip learners for real-world big data challenges.
What you will learn
- Describe Spark architecture, core components, and RDD programming constructs.
- Apply transformations, persistence, and handle multiple file formats in Spark.
- Develop scalable workflows and evaluate Spark applications for optimization.