Hadoop Projects: Apply MapReduce, Pig & Hive

This course is part of Hadoop Big Data Analytics & Projects Mastery Specialization

Instructor: EDUCBA

Access provided by Michigan State University

3 modules

Gain insight into a topic and learn the fundamentals.

7 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Prepare and structure raw YouTube metadata for analysis using Hadoop and HDFS.
Apply MapReduce and Pig Latin scripts to process, transform, and analyze large YouTube datasets.
Create Hive tables and execute HiveQL queries to identify top-rated and most-viewed videos.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Hadoop Big Data Analytics & Projects Mastery 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 3 modules in this course

Build practical Big Data analytics skills by analyzing real-world YouTube metadata with Hadoop, MapReduce, Apache Pig, and Apache Hive. You’ll begin by preparing and structuring raw YouTube datasets, exploring Big Data fundamentals, and applying MapReduce jobs to identify patterns and high-rated videos.

Next, you’ll use Pig Latin commands and scripts to design scalable data transformation workflows. You’ll process YouTube metadata, store results in HDFS, and interpret structured outputs. You’ll then create Hive tables and execute HiveQL queries to generate aggregated insights, including top-rated and most-viewed videos. Designed for students, professionals, and data enthusiasts, this project-based course emphasizes end-to-end implementation rather than theory alone. You’ll connect data preparation, large-scale processing, transformation, querying, and output interpretation in one structured workflow. By completing the course, you’ll be able to apply key Hadoop ecosystem tools efficiently and build a portfolio-ready YouTube data analysis project that demonstrates practical Big Data skills.

This module introduces learners to the fundamentals of Hadoop and MapReduce by exploring YouTube data analysis scenarios. It covers data preparation, Big Data basics, and the use of MapReduce to process large-scale datasets. Learners will gain hands-on insights into building analyzers that identify key patterns, high-rated videos, and structured outputs.

What's included

10 videos3 assignments

10 videosTotal 87 minutes

Introduction to Youtube Data Analysis Using Hadoop7 minutes
Introduction to Youtube Data Analysis Using Hadoop Continues8 minutes
Data Preparation For Youtube Data Analysis using Hadoop8 minutes
Basics of Big Data and Map Reduce10 minutes
More on Big Data and Map Reduce7 minutes
Working with Analysis Senario using Map Reduce10 minutes
Example of Youtube Analyser using Map Reduce9 minutes
Output Youtube Analyse in Map Reduces7 minutes
High Rated Youtube Video Analyser in Map Reduces12 minutes
Implementation and Outputt in Map Reduces10 minutes

3 assignmentsTotal 50 minutes

Introduction and Data Preparation10 minutes
YouTube Analysis with MapReduce10 minutes
Graded - Hadoop and MapReduce Foundations30 minutes

This module explores Apache Pig as a high-level tool for simplifying data transformations in Hadoop. Learners will understand Pig Latin scripting, its commands, and how to use Pig for analyzing YouTube metadata. Practical examples will demonstrate how Pig outputs structured insights from large datasets.

What's included

5 videos3 assignments

This module covers Apache Hive and its SQL-like language HiveQL for large-scale YouTube data analysis. Learners will practice creating tables, running Hive queries, and generating aggregated insights such as top-rated or most-viewed videos. The module concludes with a summary of integrating Hadoop, MapReduce, Pig, and Hive for scalable data analytics.