Overview

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Build practical skills in Hadoop, HDFS, Hive, Pig, MapReduce, and Sqoop by completing four real-world Big Data projects. You’ll analyze customer complaints, health surveys, traffic violations, and loan datasets while learning how to import, organize, transform, process, and export large-scale data. Beginning with customer complaint analysis, you’ll build Hive-based workflows, configure driver and JAR files, run MapReduce programs, and identify geographic patterns. You’ll then manage health survey data in HDFS and transfer datasets between Hadoop and relational databases. A traffic violation project will strengthen your ability to use Sqoop, execute MapReduce jobs, and extract results for reporting. Finally, you’ll prepare loan data, calculate average risk, and compare risk across loan types, categories, and locations using Pig and MapReduce. Designed for learners seeking hands-on Hadoop and Big Data analytics experience, this course combines four complete case studies with step-by-step implementation. By the end, you’ll be able to build custom MapReduce logic, integrate Pig and Hive for advanced analysis, optimize sorting, filtering, and aggregation, and manage data exchange between RDBMS and Hadoop systems. Enroll to develop practical experience applying Hadoop workflows to structured, real-world datasets.

Syllabus

Getting Started with Hadoop Projects
- This module introduces learners to Hadoop through a real-world customer complaint analysis project. It explains how to design Hive-based workflows, configure driver and JAR files, and perform data analysis using MapReduce to uncover complaint patterns and location-based insights.
Managing Health Survey Data using HDFS
- This module focuses on working with health-related datasets using HDFS and SQL integration. Learners explore how to import, organize, and export data efficiently between relational databases and Hadoop for effective population-level health analysis.
Traffic Violation Data Analysis
- This module walks through a complete Hadoop-based project on traffic violation data. Learners gain practical experience importing data from SQL into HDFS, executing MapReduce jobs, and extracting analytical results for decision-making and reporting.
Loan Dataset Analysis using Pig and MapReduce
- This module explores advanced data analysis using Pig and MapReduce. Learners work with a loan dataset to calculate average risk metrics, implement distributed computations, and evaluate risk by loan type, category, and geographic region.
Advanced Hadoop Integrations and Analysis
- This module deepens learner expertise through advanced integrations and optimizations. It covers custom MapReduce development, Pig–Hive combined analysis, and the use of Sqoop for seamless data exchange between RDBMS and Hadoop systems.