Apache Spark: Apply & Evaluate Big Data Workflows

This course is part of Spark and Python for Big Data with PySpark Specialization

Instructor: EDUCBA

Access provided by American Open University Global

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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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Gain a foundational understanding of a subject or tool
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There are 2 modules in this course

Learn the fundamentals of Apache Spark and build a strong foundation in big data processing and distributed computing. This beginner-friendly course introduces Spark's architecture, core components, and Resilient Distributed Datasets (RDDs), then guides you through advanced RDD transformations, persistence strategies, Pair RDD operations, and working with data formats such as CSV, JSON, Parquet, and Avro.

Designed for beginners who want to understand scalable data processing, this course combines conceptual explanations with hands-on examples to help you confidently explore Spark's core APIs and workflows. You'll learn how Spark uses distributed and in-memory processing, perform data transformations and actions, optimize applications through persistence, and evaluate storage formats for efficient data handling. By the end of the course, you'll be able to analyze Apache Spark applications, evaluate data storage strategies, and develop scalable data processing workflows using core Spark concepts and APIs. Whether you're starting your journey in big data or building a foundation for Spark-based analytics, this course provides a structured path to understanding essential Apache Spark workflows.

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.