What you'll learn

Process large YouTube datasets using MapReduce, Pig, and Hive.
Apply Pig Latin and HiveQL for metadata and insight analysis.
Design end-to-end Big Data workflows for real-world projects.

Skills you'll gain

Details to know

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Assessments

9 assignments

Taught in English

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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.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

By the end of this course, learners will be able to prepare raw YouTube datasets, apply MapReduce for large-scale processing, implement Pig Latin scripts for metadata analysis, and execute HiveQL queries to generate structured insights. The course blends practical scenarios with hands-on tools from the Hadoop ecosystem, empowering learners to analyze real-world data efficiently.

This project-based course offers a unique opportunity to practice Big Data analytics using actual YouTube data. Unlike theoretical courses, it emphasizes end-to-end implementation — from data preparation and transformation to query execution and output interpretation. Learners will gain practical skills in Hadoop, MapReduce, Pig, and Hive, making them proficient in handling complex datasets and extracting valuable insights. By completing this course, learners will not only master essential Hadoop tools but also build a portfolio-ready project that demonstrates Big Data analysis skills applicable to industry scenarios such as video analytics, recommendation systems, and large-scale reporting. This makes the course ideal for students, professionals, and data enthusiasts aiming to strengthen their expertise in Big Data.

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 Continues7 minutes
Data Preparation For Youtube Data Analysis using Hadoop8 minutes
Basics of Big Data and Map Reduce10 minutes
More on Big Data and Map Reduce6 minutes
Working with Analysis Senario using Map Reduce10 minutes
Example of Youtube Analyser using Map Reduce8 minutes
Output Youtube Analyse in Map Reduces6 minutes
High Rated Youtube Video Analyser in Map Reduces12 minutes
Implementation and Outputt in Map Reduces9 minutes

3 assignmentsTotal 50 minutes

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