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Apache Spark with Scala: Master Data Building & Analysis

EDUCBA via Coursera

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Overview

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Build a strong foundation in Apache Spark with Scala and learn how to develop scalable big data applications from core concepts to real-time data processing. This course introduces Spark architecture, its integration with YARN, and essential Scala programming concepts, including variables, functions, loops, collections, traits, abstract classes, exception handling, and access modifiers. As you progress, you'll work with Resilient Distributed Datasets (RDDs), differentiate transformations and actions, and implement Spark Streaming with windowing and checkpointing for fault-tolerant real-time data processing. You'll also construct a Spark project using Maven, integrate external APIs such as Twitter, and evaluate Hadoop 1.x and 2.x for effective resource management in Spark environments. Designed for aspiring data engineers, big data developers, and learners interested in distributed data processing, this course combines Scala programming with practical Apache Spark workflows. By the end of the course, you'll be able to apply Scala fundamentals, optimize RDD operations, implement reliable streaming pipelines, and build end-to-end Spark applications for scalable data analysis.

Syllabus

Scala Foundations and Spark Basics

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.

Advanced Spark and Real-Time Applications

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.