Apache Spark with Scala: Master Data Building & Analysis

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

Instructor: EDUCBA

Access provided by Lok Jagruti University

2 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply Scala fundamentals including variables, functions, and advanced concepts.
Implement Spark RDD operations, streaming, and fault-tolerant pipelines.
Build real-time big data solutions integrating Spark with external systems.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

This course provides a complete journey into Apache Spark with Scala, designed for learners who want to analyze, design, implement, and evaluate big data applications. Beginning with the foundations of Spark architecture and Scala programming, learners will explore variables, functions, collections, and advanced Scala concepts such as traits, abstract classes, and exception handling. The course then advances into Spark RDD operations, streaming, windowing, and checkpointing, helping learners apply distributed transformations and implement real-time data pipelines. Finally, learners will construct integrated projects using Maven, connect Spark to external systems like Twitter APIs, and evaluate the impact of Hadoop 1.x vs 2.x in managing resources for scalable applications.

This module introduces learners to the fundamentals of Apache Spark and the Scala programming language, equipping them with the foundational knowledge to build and manage big data applications. Starting with an overview of Spark’s architecture, flow, and integration with YARN, the module progresses to Scala essentials, covering variables, functions, loops, and collections. It then advances into key Scala concepts such as abstract classes, traits, exception handling, and access modifiers. By the end of this module, learners will be able to confidently apply Scala programming constructs within Spark environments to process and analyze data efficiently.

What's included

24 videos4 assignments

24 videos Total 188 minutes

Introduction to Connecting to Twitter Using Spark 9 minutes
Flowchart of Spark 6 minutes
Components of Spark 8 minutes
Different Services Running on YARN 8 minutes
Introduction to Scala 6 minutes
Case Classes and Pattern Matching 7 minutes
Installation of Scala 7 minutes
Variables and Functions 7 minutes
Variables and Functions Continues 5 minutes
Loops 12 minutes
Collections 11 minutes
More on Collections 8 minutes
Abstract Class 8 minutes
Example of the Abstract Class 4 minutes
Trait 6 minutes
Example of the Trait 6 minutes
Exception 6 minutes
Practical Example of Exceptions 7 minutes
Customize Exceptions of Scala Project 9 minutes
Modifiers 11 minutes
Strings 11 minutes
Methods in Strings 10 minutes
Methods in Strings Continue 6 minutes
Array 10 minutes

4 assignments Total 60 minutes

Spark Overview and Setup 10 minutes
Scala Language Essentials 10 minutes
Advanced Scala Concepts 10 minutes
Scala Foundations and Spark Basics 30 minutes

This module explores the advanced features of Apache Spark, focusing on Resilient Distributed Datasets (RDDs), Spark Streaming, and real-time application integration. Learners will understand how to perform transformations and actions on RDDs, process live streaming data, and implement checkpointing for fault tolerance. The module also covers integration with external systems such as Twitter, project setup using Maven and Scala, and explains the differences between Hadoop 1.x and 2.x for Spark compatibility. By completing this module, learners will gain the ability to build scalable, fault-tolerant, real-time big data applications using Spark and Scala.