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⇱ Hadoop and Spark Fundamentals: Unit 2 | Coursera

Hadoop and Spark Fundamentals: Unit 2

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Hadoop and Spark Fundamentals: Unit 2

Included with

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand and implement Hadoop MapReduce for distributed data processing, including compiling, running, and debugging applications.

  • Apply advanced MapReduce techniques to real-world scenarios such as log analysis and large-scale text processing.

  • Utilize higher-level tools like Apache Pig and Hive QL to streamline data workflows and perform complex queries.

  • Gain hands-on experience with Apache Spark and PySpark for modern, scalable data analytics.

Details to know

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Assessments

4 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Hadoop and Spark Fundamentals 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 is 1 module in this course

This course introduces the fundamentals of modern data processing for data engineers, analysts, and IT professionals. You will learn the basics of Hadoop MapReduce, including how it works, how to compile and run Java MapReduce programs, and how to debug and extend them using other languages. The course includes practical exercises such as word counts across multiple files, log file analysis, and large-scale text processing with datasets like Wikipedia. You will also cover advanced MapReduce features and use tools like Yarn and the Job Browser. The course then covers higher-level tools such as Apache Pig and Hive QL for managing data workflows and running SQL-like queries. Finally, you will work with Apache Spark and PySpark to gain experience with modern data analytics platforms. By the end of the course, you will have practical skills to work with big data in various environments.

This module introduces the core components of big data processing with Hadoop and Spark. It covers the fundamentals of Hadoop MapReduce, including its operation, programming, and debugging, followed by practical examples such as word count, log analysis, and benchmarking. The module then explores higher-level tools like Apache Pig and Hive for simplified data processing. Finally, it introduces Apache Spark and its Python interface, PySpark, highlighting Spark’s growing role in data analytics.

What's included

20 videos4 assignments

20 videos•Total 241 minutes
  • Learning objectives•1 minute
  • Understand the MapReduce paradigm•8 minutes
  • Develop and run a Java MapReduce application•16 minutes
  • Understand how MapReduce works•20 minutes
  • Learning objectives•1 minute
  • Use the Streaming Interface•11 minutes
  • Use the Pipes interface•7 minutes
  • Run the Hadoop grep example•6 minutes
  • Debugging MapReduce•11 minutes
  • Understand Hadoop Version 2 MapReduce•8 minutes
  • Use Hadoop Version 2 features—Part 1•21 minutes
  • Use Hadoop Version 2 features—Part 2•18 minutes
  • Learning objectives•1 minute
  • Demonstrate a Pig example•8 minutes
  • Demonstrate a Hive example•7 minutes
  • Demonstrate an Oozie example—Part 1•29 minutes
  • Demonstrate an Oozie example—Part 2•17 minutes
  • Learning objectives•1 minute
  • Learn Spark language basics•40 minutes
  • Demonstrate a PySpark command line example•12 minutes
4 assignments•Total 120 minutes
  • Hadoop MapReduce Quiz•30 minutes
  • Hadoop MapReduce Examples Quiz•30 minutes
  • Higher Level Tools Quiz•30 minutes
  • Using the Spark Language Quiz•30 minutes

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Instructors

Pearson
268 Courses•67,407 learners

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Frequently asked questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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