adplus-dvertising
frame-decoration

Question

How do Distributed Key-Value and Column-oriented DBMS differ from traditional databases?

a.

They rely on fixed schema-based structures.

b.

They prioritize data distribution and scalability.

c.

They do not use MapReduce for data processing.

d.

They focus on analytical processing by row.

Posted under Big Data Computing

Answer: (b).They prioritize data distribution and scalability. Explanation:Distributed Key-Value and Column-oriented DBMS prioritize distribution and scalability, unlike traditional databases.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

Q. How do Distributed Key-Value and Column-oriented DBMS differ from traditional databases?

Similar Questions

Discover Related MCQs

Q. What distinguishes Grid computing from MapReduce in terms of data processing?

Q. What advantage does MapReduce offer to programmers compared to Grid computing?

Q. Which popular column-oriented DBMS uses its own implementation of MapReduce internally?

Q. What is a typical use case for shared-memory parallel programming environments like OpenMP?

Q. How do shared-memory parallel programming interfaces like OpenMP compare to MapReduce in terms of flexibility and ease of use?

Q. In the MapReduce model, what is the purpose of the map() function?

Q. How are key-value pairs processed in the reduce() function in the MapReduce model?

Q. In the MapReduce word count application, what is the purpose of the map() function?

Q. What is the primary task of the reduce() function in the word count application?

Q. What is the purpose of the intermediate key-value pairs produced during the map() function in the word count application?

Q. How are key-value pairs processed during the reduce() function in the word count application?

Q. In which scenario would implementing a distributed version of grep using MapReduce be straightforward?

Q. How is the map stage of the sorting problem different from the searching problem in MapReduce?

Q. Why is MapReduce a suitable choice for maintaining and updating search engine indices?

Q. What data structure is commonly used for information retrieval, and how is it implemented with MapReduce?

Q. Why are logs a good fit for MapReduce processing?

Q. What is the primary advantage of using MapReduce for log analysis?

Q. How does MapReduce handle logs that are not entirely structured?

Q. In the context of MapReduce, what is meant by "embarrassingly parallel problems"?

Q. Which major search engines are known to use MapReduce for various tasks?