To process large data sets quickly, big data architectures use.
Distributed computing
Cluster computing
Parallel computing
All of the mentioned above
Correct Answer :
D. All of the mentioned above
Distributed computing, also known as cluster computing, or parallel computing, is a technique for processing massive data sets quickly. It is based on multiprocessor servers performing various calculations at the same time to achieve this. It is possible to solve large issues by breaking them down into smaller components that can be solved in parallel.
Related Questions
_____ is the supporting physical infrastructure is fundamental to the operation and scalability of big data architecture.
Redundant physical infrastructure
Integrated System
Integrated Database
All of the mentioned above
Amongst which of the following is/are correct.
Default location of Hadoop configuration is in $HADOOP /conf/ HOME
If $HADOOP HOME is specified, Sqoop will utilise the default installation location
default location of Hadoop configuration is in $HADOOP HOME/conf/
Sqoop command-line tool serves as a wrapper for the bin/hadoop script that is included with Hadoop as a base.
To process large data sets quickly, big data architectures use.
Distributed computing
Cluster computing
Parallel computing
All of the mentioned above
The goal of most big data solutions is to provide insights into the data through _____.
Hive
HBase
Analysis and reporting
All of the mentioned above
Hypervisor is a practical approach of getting things virtualized _______.
Quickly and efficiently
Systematic approach
Both A and B
All of the mentioned above
Amongst which of the following is/are not Big Data Technologies?
Apache Hadoop
Apache Spark
Apache Kafka
Apache Pytarch
Amongst which of the following is/are represents the Value of data in Big Data environment,
Worth in information
Useless data
Useless information
None of the mentioned above
Amongst which of the following is / are true with reference to hypothesis?
A statement that the researcher wishes to put to the test using the information gathered during a study.
A research question that will be answered as a result of the findings.
A theory that serves as the foundation for the research.
the application of statistics to determine the extent to which the outcomes could have been caused by chance
In Big Data environments, Variety of data includes
Includes multiple formats and types of data
Includes structured data in the form of financial transactions,
Includes semi-structured data in the form of emails and unstructured data in the form of images
All of the mentioned above
Data interpretation refers -
Process of attaching meaning to the data
Convert text into insightful information
Effective conclusion
All of the mentioned above
______ is best described as a programming model that is used to construct Hadoop-based applications that can be scaled up and down.
Oozie
Zookepper
MapReduce
All of the mentioned above
Analytics has begun to impact almost every industry including,
Healthcare,
Education and telecom
Telecom
All of the mentioned above
Prescriptive analytics makes the use of machine learning to help ____ to decide a course of action based on a computer program's predictions.
Business organizations
System development
Employees development
All of the mentioned above
______ involves the simultaneous execution of multiple sub-tasks that collectively comprise a larger task.
Parallel data processing
Single channel processing
Multi data processing
None of the mentioned above
Database requirements for operational data includes ___.
Indexing and Cataloging, Replication
File Storage and Structure, Query Processing
Transactions Support
All of the mentioned above
Prescriptive analytics is a process that _____ and provides instant recommendations on how to optimize business practices.
Analyzes data
Data storage
Data ingestion
None of the mentioned above
The Hadoop framework is built in Java, which means that MapReduce applications do not need to be written in _____.
C#
C
Java
None of the mentioned above
Big Data Ingestion Layer concerned with the,
Big Data ingestion pipeline is divided into different layers
Each layer performs a particular function
Both A and B
None of the mentioned above
_____ is a platform for developing data flows for the extraction, transformation, and loading (ETL) of huge datasets, as well as for data analysis.
Spark
HBase
Hive
Pig
Variety describes one of the biggest challenges of ______.
Big data
Data science
Data integration
None of the mentioned above
Data Query Layer is the _____ where active analytic processing of Big Data takes place.
Architectural layer
Fundamental layer
Backpropogation layer
None of the mentioned above
Query processing system refers to the entire process from translating a ___ to the database system.
Query
Statement
Function
None of the mentioned above
An ____ consisted of highly structured data managed by the line of business in a relational database.
Operational data source
Qualitative data source
Both A and B
None of the mentioned above
Amongst which of the following is /are most suitable with reference to the data collector layer,
Transportation of data from the ingestion layer to the rest of the data pipeline
Data storage
Data identification
None of the mentioned above
The architecture should designed in such a way so that it can handle ______.
Data ingestion
Data processing
Data analysis
All of the mentioned above
Custom extensions built in the ____ programming language are also supported by Hive.
Java
C#
C
C++
In order to interact with HDFS, a command line interface named _____ is provided.
HDFS Shell
DFS Shell
K Shell
FS Shell
The _______ is the default partitioned in Hadoop, and it offers a method called getPartition that allows us to partition data.
HashPartitioner
Map function
Reduce function
All of the mentioned above
Amongst which of the following is / are the goodness of prescriptive analytics,
Exhausting valuable resources on housing data that does not inform business decisions
Spending time sifting through unutilized data sets
Missing out on unique revenue streams and insights
All of the mentioned above
In Big Data environments, Velocity refers
Data can arrive at fast speed
Enormous datasets can accumulate within very short periods of time
Velocity of data translates into the amount of time it takes for the data to be processed
All of the mentioned above