Big Data: Volume of Data
Generated
Examples & Consequences
Social media platforms (Facebook, Twitter,
Instagram) generate a massive volume of data
every second
Consequence: Need for efficient storage
and processing solutions
Internet of Things (IoT)devices (smart home
devices, fitness trackers, industrial machines)
generate vast amounts of data
Consequence: Potential for valuable
insights, but also increased security risks
Online transactions and financial data
Consequence: Opportunities for fraud
detection, but also increased vulnerability
to cyber attacks
Classifying Data as Big Data:
The Five Vs
Volume: Amount of data generated
Velocity: Speed of data generation and
processing
Variety: Types of data (structured, semi-
structured, unstructured)
Veracity: Accuracy and quality of data
Value: Potential usefulness of data
Hadoop: A Framework for
Storing & Processing Big Data
, Open-source framework for storing and
processing large datasets
Allows for distributed storage and processing on
clusters of servers
Key components: Hadoop Distributed File System
(HDFS), MapReduce, YARN
Big Data: Overview &
Importance
Defined as extremely large datasets that may be
analyzed computationally to reveal patterns,
trends, and associations
Enables organizations and individuals to make
data-driven decisions
Applications in various fields including healthcare,
finance, marketing, and transportation
Critical to the development of smart cities,
autonomous vehicles, and personalized medicine.
Volume of Data Generated:
Examples and Their
Consequences
Classifying Data as Big Data: The Concept
of the 'Five Vs'
Big data is classified using the concept of the "five Vs."
These are:
Volume: The amount of data that is being
generated and stored
Velocity: The speed at which new data is being
generated and processed
Variety: The different types of data that are
being generated and stored
Generated
Examples & Consequences
Social media platforms (Facebook, Twitter,
Instagram) generate a massive volume of data
every second
Consequence: Need for efficient storage
and processing solutions
Internet of Things (IoT)devices (smart home
devices, fitness trackers, industrial machines)
generate vast amounts of data
Consequence: Potential for valuable
insights, but also increased security risks
Online transactions and financial data
Consequence: Opportunities for fraud
detection, but also increased vulnerability
to cyber attacks
Classifying Data as Big Data:
The Five Vs
Volume: Amount of data generated
Velocity: Speed of data generation and
processing
Variety: Types of data (structured, semi-
structured, unstructured)
Veracity: Accuracy and quality of data
Value: Potential usefulness of data
Hadoop: A Framework for
Storing & Processing Big Data
, Open-source framework for storing and
processing large datasets
Allows for distributed storage and processing on
clusters of servers
Key components: Hadoop Distributed File System
(HDFS), MapReduce, YARN
Big Data: Overview &
Importance
Defined as extremely large datasets that may be
analyzed computationally to reveal patterns,
trends, and associations
Enables organizations and individuals to make
data-driven decisions
Applications in various fields including healthcare,
finance, marketing, and transportation
Critical to the development of smart cities,
autonomous vehicles, and personalized medicine.
Volume of Data Generated:
Examples and Their
Consequences
Classifying Data as Big Data: The Concept
of the 'Five Vs'
Big data is classified using the concept of the "five Vs."
These are:
Volume: The amount of data that is being
generated and stored
Velocity: The speed at which new data is being
generated and processed
Variety: The different types of data that are
being generated and stored
