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Questions and Answers
What is the term used for individual servers within a Hadoop cluster?
What is the term used for individual servers within a Hadoop cluster?
- Nodes (correct)
- Clustered servers
- Commodity servers
- Redundant servers
How is scalability achieved in Hadoop?
How is scalability achieved in Hadoop?
- By increasing the size of individual servers
- By adding more nodes to the cluster (correct)
- By reducing the data replication
- By using specialized hardware
What is the primary method used to handle hardware failures in a Hadoop cluster?
What is the primary method used to handle hardware failures in a Hadoop cluster?
- Replacing the failed hardware with new components
- Replicating files across nodes in the cluster (correct)
- Using specialized software to prevent failures
- Manually transferring data from the failed node to another node
What happens to failed tasks in Hadoop's data processing jobs?
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?
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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