alleryStart learning →How does the framework define horizontal scaling?
AIncreasing capacity of output by improving the existing components
BReducing the dataset by sampling it before any analysis is begun
CReapplying an entity or an action in order to achieve another goal
DIncreasing capacity of output by adding contributing resources
Answer & Solution
Correct answer: D. Increasing capacity of output by adding contributing resources
1. The definition is short: "Horizontal scaling is increasing the capacity of production output through the addition of contributing resources."
2. The word that matters is addition. Nothing is improved; more of it is added.
3. The framework calls this "data-intensive parallel processing" and says it "is at the heart of the Big Data revolution", achieved through "the maturation of techniques for distributed processing across independent resources, or nodes in a cluster".
4. Improving existing components is the definition of vertical scaling, the other method.
5. Sampling before analysis is a statistical practice the framework discusses under data science, not a scaling method. Reapplying an entity for a different purpose is its definition of reusability.
6. The framework states the ideal that horizontal scaling aims at: "twice the data can be processed in the same amount of time using twice the number of data nodes", which it calls linear scalability.
_Source: NIST SP 1500-1r2 "NIST Big Data Interoperability Framework: Volume 1, Definitions" (Oct 2019), p.10 section 3.1 "Big Data Definitions"_
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