NoSQL Databases: Analyze & Implement Scalable Systems

Overview

By the end of this course, learners will be able to explain the origins of NoSQL databases, evaluate their features and data models, compare ACID and BASE consistency approaches, apply workflow orchestration with Apache Oozie, and implement real-time stream processing using Apache Storm. They will also design recommendation systems, apply classification techniques, and implement clustering algorithms with Apache Mahout. This course equips learners with both foundational knowledge and hands-on skills in distributed big data systems. Through a structured progression, learners gain practical experience with tasks, workers, topologies, and coordinators, while also exploring advanced topics such as data versioning, stream reliability, and scalable machine learning models. What makes this course unique is its integration of multiple cutting-edge technologies—NoSQL, Oozie, Storm, and Mahout—into a single, cohesive learning journey. Instead of studying these tools in isolation, learners will analyze how they interact in real-world scenarios to build scalable, fault-tolerant, and intelligent data solutions. Ideal for aspiring data engineers, developers, and analysts, this course provides the skills to design, evaluate, and implement modern big data architectures that drive insights and innovation.

Syllabus

Foundations of NoSQL Databases

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.

Workflow Orchestration with Apache Oozie

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.

Real-Time Processing with Apache Storm

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.

Machine Learning with Apache Mahout

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.