Big Data Analytics Interview Questions

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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

1.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

2.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

3.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.

4.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

5.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.

6.What Is The Difference Between Data Mining And Data Profiling?

The main difference between data mining and data profiling is as follows:

Data profiling-It targets the instant analysis of individual attributes like price vary, distinct price and their frequency, an incidence of null values, data type, length, etc.

Data mining-It focuses on dependencies, sequence discovery, relation holding between several attributes, cluster analysis, detection of unusual records etc.

7.What Are The Most Common Analytical Technique Categories?

Most of the widely used analytical techniques falls into one of the following categories:

  • Statistical Methods
  • Forecasting
  • Regression Analysis
  • Database Querying
  • Data Warehouse
  • Machine Learning & Data Mining

8.What is the K-mean Algorithm?

K-mean is a partitioning technique in which objects are categorized into K groups. In this algorithm, the clusters are spherical with the data points aligned around that cluster, and the variance of the clusters is similar to one another.

9.What Are The Sources Of Unstructured Data In Big Data?

The sources of Unstructured data are as follows

  • Textfiles & Documents
  • Server Website & Application Log
  • Sensor Data
  • Images, Videos & Audio Files
  • Emails
  • Social Media Data

10.What Is Data Cleansing?

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

11.Determine The Most Common Statistical Approaches For Data Analysis.

  •  Simplex algorithm
  • Bayesian approach
  • Markov chains
  • Mathematical optimization
  • Cluster and spatial processes
  • Rank statistics

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