This module introduces learners to the origins, features, and benefits of NoSQL databases. It explores schema flexibility, consistency models, and application development, while also introducing concepts like data versioning and workflow orchestration. Learners build a strong foundation to understand why NoSQL emerged as a solution for big data and distributed systems.

This module provides hands-on insights into Apache Oozie for workflow orchestration in big data environments. Learners examine Hive and Pig actions, control nodes, coordinators, and workflow applications. The module also introduces Apache Storm basics, stream processing, and reliability concepts essential for modern big data solutions.

This module dives deeper into Apache Storm, covering tasks, workers, deployment, and parallelism. It bridges Storm’s real-time processing with Apache Mahout’s machine learning capabilities, focusing on recommendations, classifiers, and practical examples. Learners gain practical skills in deploying, scaling, and integrating real-time ML applications.

This module focuses on machine learning algorithms implemented in Apache Mahout. Learners study recommendation systems, clustering, classification, evaluation techniques, and advanced algorithms like KMeans and Logistic Regression. By the end, learners will be able to design and implement scalable ML models on big data platforms.