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.