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.
NoSQL Databases: Analyze & Implement Scalable Systems
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NoSQL Databases: Analyze & Implement Scalable Systems
This course is part of Hadoop & Big Data Foundations Mastery Course Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Compare NoSQL models and consistency approaches.
Implement workflows with Oozie and streaming via Storm.
Build recommendation and clustering models using Mahout.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
November 2025
Assessments
19 assignments
Taught in English
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This course is part of the Hadoop & Big Data Foundations Mastery Course Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 4 modules in this course
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.
What's included
21 videos5 assignments
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.
What's included
23 videos5 assignments
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.
What's included
20 videos5 assignments
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.
What's included
12 videos4 assignments
Earn a career certificate
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