Questions and Answers
Why are individual computers often inadequate for handling big data?
They lack the necessary computational power and storage capacity.
What is a key benefit of resource pooling in big data clustering software?
Increased fault tolerance
How does clustering contribute to high availability in handling big data?
By providing varying levels of fault tolerance
What advantage do computer clusters offer in terms of scalability?
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In big data clustering, what is the purpose of combining the resources of many smaller machines?
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Why is it important for big data systems to emphasize real-time analytics?
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What does 'big data' refer to?
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Which of the following is NOT a characteristic of big data according to the text?
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What does 'Volume' refer to in the context of big data?
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Which 'V' in the 3V characteristics of big data focuses on the trustworthiness and accuracy of the data?
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Why is it challenging to process big datasets using traditional tools?
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Study Notes
Clustered Computing for Big Data
- Individual computers are often inadequate for handling big data due to its high storage and computational needs.
- Big data clustering software combines the resources of many smaller machines to provide benefits such as:
- Resource Pooling: combining available storage space, CPU, and memory to process large datasets.
- High Availability: providing fault tolerance and availability guarantees to prevent hardware or software failures.
- Easy Scalability: allowing for horizontal scaling by adding additional machines to the group.
What Is Big Data?
- Big data refers to a collection of data sets that are too large and complex to process using traditional database management tools or applications.
- A "large dataset" means a dataset that is too large to process or store with traditional tooling or on a single computer.
- The scale of big datasets varies significantly from organization to organization and is constantly shifting.
- Big data is characterized by the 3V's and more:
- Volume: large amounts of data (e.g., zeta bytes, massive datasets).
- Velocity: data is live streaming or in motion.
- Variety: data comes in many different forms from diverse sources.
- Veracity: the accuracy and trustworthiness of the data.
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