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Questions and Answers
What is the term used for individual servers within a Hadoop cluster?
How is scalability achieved in Hadoop?
What is the primary method used to handle hardware failures in a Hadoop cluster?
What happens to failed tasks in Hadoop's data processing jobs?
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What does HDFS stand for in the context of Hadoop?
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Study Notes
Challenges in Traditional Data Systems
- Traditional tools struggle to manage large volumes of data efficiently.
- Common issues include slow disk speeds and unreliable hardware.
- Achieving parallel processing in traditional systems can be complex.
Key Requirements for Modern Data Systems
- Reliable storage solutions to ensure data integrity.
- Powerful data processing capabilities to handle vast data sets.
- Efficient visualization methods to present data insights clearly.
Overview of Apache Hadoop
- Provides a scalable and cost-effective solution for data storage and processing.
- Designed to handle distributed processing of large data sets across clusters.
- Utilizes simple programming models for ease of use.
Features of Apache Hadoop
- Scales from single servers to thousands of machines.
- Each machine offers local computation and storage capabilities.
- Employs a self-healing mechanism to manage failures at the application layer, promoting high availability.
Architectural Inspiration
- The framework is heavily inspired by Google’s data architecture, leveraging concepts for effective data management.
Main Components of Hadoop
- Hadoop Distributed File System (HDFS): Storage component that ensures reliable, distributed storage.
- MapReduce Framework: Processing component that supports parallel processing of data.
- Hadoop Common: Utilities that underpin other modules in the Hadoop ecosystem.
- Hadoop YARN: Framework facilitating job scheduling and resource management across clusters.
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Description
Test your knowledge of Apache Hadoop and its role in addressing challenges related to scalable data storage, powerful data processing, and efficient visualization. Explore the fundamentals of the Hadoop ecosystem and its ability to handle large data sets across computer clusters.
