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Apache Spark with Scala: Master Data Building & Analysis

本课程是 Spark and Python for Big Data with PySpark 专项课程的一部分

位教师：EDUCBA

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

9 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

8 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

了解顶级公司的员工如何掌握热门技能

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Spark and Python for Big Data with PySpark 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

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.

单元详情

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.

涵盖的内容

24个视频4个作业

24个视频总计188分钟

Introduction to Connecting to Twitter Using Spark9分钟
Flowchart of Spark6分钟
Components of Spark8分钟
Different Services Running on YARN8分钟
Introduction to Scala6分钟
Case Classes and Pattern Matching7分钟
Installation of Scala7分钟
Variables and Functions7分钟
Variables and Functions Continues5分钟
Loops12分钟
Collections11分钟
More on Collections8分钟
Abstract Class8分钟
Example of the Abstract Class4分钟
Trait6分钟
Example of the Trait6分钟
Exception6分钟
Practical Example of Exceptions7分钟
Customize Exceptions of Scala Project9分钟
Modifiers11分钟
Strings11分钟
Methods in Strings10分钟
Methods in Strings Continue6分钟
Array10分钟

4个作业总计60分钟

Scala Foundations and Spark Basics30分钟
Spark Overview and Setup10分钟
Scala Language Essentials10分钟
Advanced Scala Concepts10分钟

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.