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Big Data Analytics Interview Questions

If you have engaged yourself in preparing for Big Data Analytics interview then this blog post will surely be the best aid for you in your preparation. This blog post consists of some of the most frequently asked interview questions in Big Data Analytics. Building knowledge of these questions will surely help you during the time of your interview

What Does Big Data Analytics Mean?

The term Big Data Analytics relates to the process of analyzing large volumes of data, or big data which is gathered from a wide variety of sources. Big Data Analytics enables to uncover patterns and correlations from the data that might otherwise be invisible. These insights will help in driving smart decisions for business

Explain The Five V’s  Of Big Data?

  • The five V’s of Big data is as follows:
  • Volume-This indicates the enormously rising levels of data
  • Velocity- This indicates the rate at which data grows
  • Variety- This indicates the different data types i.e. various data formats like text, audios, videos, etc
  • Veracity- This indicates the uncertainty of available data
  • Value-It refers to turning data into value

What is Clustering?

Clustering is the process in Big Data Analytics which involves grouping of similar objects into a set known as a cluster. In the process of cluster objects in one cluster are likely to be different when compared to objects grouped under another cluster. Coming to the process of statistical data analysis & data mining, clustering plays a crucial role.

List Some Tools Used For Big Data?

A wide variety of tools are used in the process of Big Data Analytics for importing, sorting, and analyzing data. The list of the tools is here below

Apache Hive, Apache Spark, MongoDB, MapReduce, Apache Sqoop, Cassandra, Apache Flume, Apache Pig, Apache Splunk, Apache Hadoop and many more

Explain The Concept Of Data Cleansing

Data cleansing is the process of removing data which is incorrect, duplicated or corrupted. This process is used for enhancing the data quality by eliminating errors and irregularities.

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