Apache Spark: Apply & Evaluate Big Data Workflows

Overview

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.

Syllabus

Getting Started with Apache Spark

This module introduces learners to the foundational concepts of Apache Spark, a powerful open-source engine designed for big data processing and analytics. Through a series of structured lessons, learners explore the Spark architecture, its core components, and essential programming constructs. The module builds a conceptual understanding of how Spark leverages distributed computing and in-memory processing, followed by a practical introduction to working with Resilient Distributed Datasets (RDDs), Spark’s core abstraction for handling data. By the end of the module, learners will be equipped with the knowledge needed to initiate basic data operations in Spark and understand its high-level architecture.

Advanced RDD Operations and Data Handling

This module deepens the learner’s understanding of Apache Spark by focusing on advanced RDD transformations, persistence strategies, operations on key-value (Pair) RDDs, and the efficient handling of diverse data formats. Learners will explore how to apply transformations like map, flatMap, and reduceByKey, understand the role and configuration of persistence levels in Spark, manipulate Pair RDDs using sorting and grouping actions, and work with commonly used file formats including CSV, JSON, Parquet, and Avro. The module equips learners with the ability to optimize Spark applications both computationally and in terms of data storage and processing.