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).
Apache Spark: Apply & Evaluate Big Data Workflows
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Apache Spark: Apply & Evaluate Big Data Workflows
This course is part of Spark and Python for Big Data with PySpark Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll 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.
Skills you'll gain
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Recently updated!
September 2025
Assessments
6 assignments
Taught in English
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This course is part of the Spark and Python for Big Data with PySpark Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course
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.
What's included
5 videos3 assignments
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.
What's included
6 videos3 assignments
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