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Questions and Answers
What is the estimated amount of data modern systems handle per day?
What is the estimated amount of data modern systems handle per day?
What is the total estimated data capacity modern systems may handle?
What is the total estimated data capacity modern systems may handle?
What new requirement is suggested for handling the increasing volume of data?
What new requirement is suggested for handling the increasing volume of data?
What is a primary challenge faced by modern distributed systems regarding data?
What is a primary challenge faced by modern distributed systems regarding data?
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Which of the following statements is true regarding modern data systems?
Which of the following statements is true regarding modern data systems?
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What is the expected outcome when additional load is added to a scalable system?
What is the expected outcome when additional load is added to a scalable system?
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What happens when resources in a system are increased?
What happens when resources in a system are increased?
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Which of the following describes a key feature of scalability in systems?
Which of the following describes a key feature of scalability in systems?
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In the context of scalability, what should NOT be the result of adding load to the system?
In the context of scalability, what should NOT be the result of adding load to the system?
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What is a misconception regarding the impact of scaling a system?
What is a misconception regarding the impact of scaling a system?
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What is a primary challenge in programming for traditional distributed systems?
What is a primary challenge in programming for traditional distributed systems?
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According to Ken Arnold, what is the defining difference between distributed and local programming?
According to Ken Arnold, what is the defining difference between distributed and local programming?
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Why do developers spend more time designing distributed systems compared to local systems?
Why do developers spend more time designing distributed systems compared to local systems?
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What complicates temporal dependencies in distributed systems?
What complicates temporal dependencies in distributed systems?
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What is suggested by the statement 'We shouldn’t be trying for bigger computers, but for more systems of computers'?
What is suggested by the statement 'We shouldn’t be trying for bigger computers, but for more systems of computers'?
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What is the primary function of the Mapper in Hadoop's MapReduce framework?
What is the primary function of the Mapper in Hadoop's MapReduce framework?
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When does the Shuffle and Sort phase occur in the MapReduce process?
When does the Shuffle and Sort phase occur in the MapReduce process?
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What type of data does the Reducer operate on in Hadoop's MapReduce model?
What type of data does the Reducer operate on in Hadoop's MapReduce model?
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Where do Map tasks typically run in relation to HDFS?
Where do Map tasks typically run in relation to HDFS?
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What is the result of the Reducer phase in MapReduce?
What is the result of the Reducer phase in MapReduce?
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What is the primary purpose of Sqoop?
What is the primary purpose of Sqoop?
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Which of the following best describes what Sqoop connects?
Which of the following best describes what Sqoop connects?
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In what scenario would you most likely use Sqoop?
In what scenario would you most likely use Sqoop?
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What is a common misconception about Sqoop's functionality?
What is a common misconception about Sqoop's functionality?
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Which prerequisite should be met before using Sqoop?
Which prerequisite should be met before using Sqoop?
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What is the primary purpose of the NameNode in this file storage system?
What is the primary purpose of the NameNode in this file storage system?
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How are data files divided in this system?
How are data files divided in this system?
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What is the default number of times a block is replicated across nodes?
What is the default number of times a block is replicated across nodes?
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Which of the following statements accurately describes block storage?
Which of the following statements accurately describes block storage?
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What type of information does the NameNode manage?
What type of information does the NameNode manage?
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What happens when a data block is corrupted in this file storage system?
What happens when a data block is corrupted in this file storage system?
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In relation to blocks, what does replication provide in this storage system?
In relation to blocks, what does replication provide in this storage system?
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Why are files split into blocks before storage in this system?
Why are files split into blocks before storage in this system?
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Study Notes
Introduction to Apache Hadoop
- Hadoop is an open-source software framework for storing, processing, and analyzing large amounts of data (big data)
- It's a distributed system, using multiple machines for a single job. This contrasts with traditional, processor-bound systems.
Hadoop Motivation
- Traditional processor-bound systems struggle with massive datasets. Processing speed is a smaller concern than getting data efficiently to the processors, a process that was often slowed by data bottlenecks.
- Hadoop addresses this by distributing the data across multiple machines, performing calculations on the data where it is already stored. This significantly speeds up processing time.
Core Hadoop Concepts
- Distributed Data: Data is distributed across multiple nodes (machines) in the cluster to avoid a central bottleneck and allow for parallel processing.
- Block Replication: Data blocks are replicated across multiple nodes to ensure data availability and fault tolerance.
- Data Locality: Processing data takes place on the node where the data is located. This reduces network data transfer required.
Hadoop Components
- HDFS (Hadoop Distributed File System): Stores data in a distributed, fault-tolerant way. Splits data into blocks and replicates them across multiple machines
- MapReduce: Processes data in a distributed manner. Breaks down complex tasks into smaller, parallel operations (map and reduce)
Hadoop Ecosystem
- Many other projects related to Hadoop make up the Hadoop ecosystem. These include Hive, Pig, HBase, and others. They provide different approaches to working with data.
Hadoop Considerations
- Scalability: Adding nodes to a Hadoop cluster increases processing capacity proportionally.
- Fault Tolerance: Hadoop automatically handles node failures, and reassigns tasks to other available nodes without significant disruption to the overall process.
- Data Formats: Data is stored in standard formats.
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Description
This quiz covers the fundamental concepts of Apache Hadoop, an open-source framework essential for processing and analyzing big data. It explores the motivation behind its development, focusing on how Hadoop solves traditional data processing issues through distributed architecture and block replication. Test your understanding of core Hadoop principles.
