The document discusses the evolution and significance of big data, outlining its characteristics, traditional approaches, and the role of technologies like Hadoop. Big data encompasses vast volumes of diverse data that require advanced processing methods, which traditional systems struggle to manage. The presentation emphasizes the importance of big data analytics in enhancing organizational efficiency and decision-making across various sectors.

1. Presented By:
Nitesh Gupta
Nimish Kochhar
Presented by:
Nitesh Gupta
Nimish Kochhar

2. Acknowledgement
 We would like to express our most sincere gratitude and appreciation to
our respected teacher Mr.Vinay Arora Sir for his guidance, patience and
encouragement throughout the development of the presentation.
 Thank you Sir for being a constant source of inspiration throughout this
tedious process.

3. Table of Contents
1. Traditional Approach
2. The Beginning
3. What is Big Data
4. Characteristic of Big Data
5. Why Big Data
6. Big Data Analytics
7. Big Players
8. Hadoop as an Example
9. Components of Hadoop
10.References

4. The Beginning…
 Big data burst upon the scene in the first
decade of the 21st century.
 The first organizations to embrace it were
online and startup firms.
 Firms like Google, eBay, LinkedIn and
Facebook were built around big data
from the beginning.
 Big Data may well be the Next Big Thing
in the IT world.
 Like many new information
technologies, big data can bring about
dramatic cost reductions, substantial
improvements in the time required to
perform a computing task and other
service offerings.

5. Traditional Approach
 In this approach, an enterprise used to have a
computer to store and process big data.
 Here data was stored in an RDBMS like Oracle
Database, MS SQL Server or DB2 .
 Sophisticated softwares were written to interact with
the database, process the required data and present
it to the users.
 This approach works well where we have less volume
of data that can be accommodated by standard
database servers.

6. What is Big Data
 ‘Big Data’ is similar to ‘small data’, but bigger in size
 Big Data refers to technologies and initiatives that involve data that is too
diverse, fast-changing or massive for conventional technologies, skills and
infra- structure to address efficiently.
 Big Data generates value from the storage and processing of very large
quantities of digital information that cannot be analyzed with traditional
computing techniques.

7. Characteristics of Big Data(4 V’s)

8. Volume
 Big data implies enormous volumes of data.
 Big Data requires processing high volumes of
low-density data, that is, data of unknown
value, such as twitter data feeds, clicks on a
web page, network traffic, sensor-enabled
equipment capturing data at the speed of
light and many more.
 Today, Facebook ingests 500 terabytes of
new data every day.
 A Boeing 737 will generate 240 terabytes of
flight data during a single flight across the US.
 Every 2 days we create as much data as we
did from the beginning of time until 2003.

9. Velocity
 It refers to the speed at which new data
is generated and the speed at which
data moves around.
 Big data technology now allows us to
analyse the data while it is being
generated without ever putting it into
databases.
 Machine to machine processes
exchange data between billions of
devices.
 Infrastructure and sensors generate
massive log data in real-time.
 On-line gaming systems support millions
of concurrent users, each producing
multiple inputs per second.

10. Variety
 It refers to the many sources and types of data both structured and
unstructured.
 Traditional database systems were designed to address smaller volumes of
structured data, fewer updates or a predictable, consistent data structure.
 Now data comes in the form of emails, photos, videos, monitoring devices,
PDFs, audio, etc. This variety of unstructured data creates problems for
storage, mining and analyzing data.
 The real world have data in many different formats and that is the
challenge we need to overcome with the Big Data.

11. Veracity
 Veracity refers to the messiness or trustworthiness of the data.
 With many forms of big data, quality and accuracy are less controllable,
for example Twitter posts with hashtags, abbreviations, typos and colloquial
speech.
 Big data and analytics technology now allows us to work with these types
of data. The volumes often make up for the lack of quality or accuracy.

12. Sources of Big Data
Today organizations are utilizing, sharing and storing
more information in varying formats including:
 E-mail and Instant Messaging
 Social media channels
 Video and audio files
This unstructured data adds up to as much as 85% of the
information that businesses store.
The ability to extract high value from this data to enable
innovation and competitive gain is the purpose of Big
Data analytics.

13. Big Data Analytics
 Big data is really critical to our life and its emerging as
one of the most important technologies in modern
world.
 Using the information kept in the social networking sites
like Facebook, the marketing agencies are learning
about the response for their campaigns, promotions and
other advertising mediums.
 Analyzing the data like preferences and product
perception of their consumers, product companies and
retail organizations are planning their production.
 Using the data regarding the previous medical history of
patients, hospitals are providing better and quick
service.

14. Big Players

15. Hadoop
 Hadoop is an open-source framework that allows to store and
process big data in a distributed environment across clusters of
computers using simple programming models.
 It is designed to scale up from single servers to thousands of
machines, each offering local computation and storage.
 Doug Cutting took the solution provided by Google and started an
Open Source Project called HADOOP in 2005
 Operates on unstructured and structured data.
 A large and active ecosystem.
 Open source under the Apache License.

17. Hadoop Distributed File System
 Data is organized into files and directories
 Files are divided into blocks,distributed across nodes.
 Blocks replicated to handle failure
 Reliable,redundant,distributed file system optimized for large files

18. MapReduce
 The MapReduce framework consists of a single JobTracker and several
TaskTrackers in a cluster.
 The JobTracker is responsible for resource management, tracking resource
consumption/availability and scheduling the job component tasks onto the
data nodes.
 The TaskTracker execute the tasks as directed by the JobTracker and
provide task-status information periodically.

19. References
 https://www.mongodb.com/big-data-explained
 https://en.wikipedia.org/wiki/Big_data
 www.tutorialspoint.com/hadoop/hadoop_big_data_overview.ht
m
Books-
 Big Data: A Revolution by Viktor Mayer-Schonberger
 Hadoop: The Definitive Guide by Tom White