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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您将学到什么
Compare NoSQL models and consistency approaches.
Implement workflows with Oozie and streaming via Storm.
Build recommendation and clustering models using Mahout.
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November 2025
作业
19 项作业
授课语言：英语（English）
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本课程是 Hadoop & Big Data Foundations Mastery Course 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得对主题或工具的基础理解
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该课程共有4个模块
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.
涵盖的内容
21个视频5个作业
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.
涵盖的内容
23个视频5个作业
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.
涵盖的内容
20个视频5个作业
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.
涵盖的内容
12个视频4个作业
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