Overview

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Build the skills to analyze, apply, and implement scalable data systems using NoSQL databases, Apache Oozie, Apache Storm, and Apache Mahout. You’ll begin by exploring the origins and benefits of NoSQL, including schema flexibility, diverse data types, data versioning, and the role of NoSQL in managing large-scale, unstructured data. You’ll also compare ACID and BASE consistency models and apply consistency principles to application development. Next, you’ll design and schedule big data workflows with Apache Oozie using Hive actions, control nodes, coordinators, and workflow applications. You’ll then use Apache Storm for real-time stream processing, working with topologies, stream groupings, tasks, workers, Zookeeper, deployment, parallelism, and reliability mechanisms. Finally, you’ll apply Apache Mahout to scalable machine learning workflows. You’ll design recommendation systems, use classification and clustering techniques, evaluate model performance, and work with Canopy, Naïve Bayes, KMeans, and Logistic Regression. Designed for aspiring data engineers, developers, and analysts, this course uniquely connects database design, workflow orchestration, real-time processing, and machine learning in one structured journey. Enroll to gain practical skills for building scalable, fault-tolerant, and intelligent big data solutions.

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.