Big data refers to large, complex datasets that are difficult to process using traditional database management tools. There are four key characteristics of big data: volume, velocity, variety, and veracity. Various sources generate big data, including social media, scientific instruments, mobile devices, sensors, and more. Analyzing big data can provide benefits like cost reductions, time reductions, new product development, and smarter business decisions. Hadoop Distributed File System (HDFS) and Hadoop software are commonly used to store and analyze big data across clusters of commodity servers in a scalable, cost-effective manner.

1. Big Data

3. Big Data
• What is Big Data?
• Analog starage vs digital.
• The FOUR V’s of Big Data.
• Who’s Generating Big Data
• The importance of Big Data.
• Optimalization
• HDFC

4. Definition
Big datais the term for a collection
of data sets so large and complex
that it becomes difficult to
process using on-hand database
management tools or traditional
data processing applications. The
challenges include capture,
curation, storage, search,
sharing, transfer, analysis, and
visualization.

6. The FOUR V’s of Big Data
From traffic patterns and music downloads to web
history and medical records, data is recorded,
stored, and analyzed to enable that technology
and services that the world relies on every day.
But what exactly is big data be used?
According to IBM scientists big data can be break
into four dimensions: Volume, Velocity, Variety
and Veracity.

7. The FOUR V’s of Big Data

8. The FOUR V’s of Big Data
Volume. Many factors contribute to the increase in
data volume. Transaction-based data stored
through the years. Unstructured data streaming
in from social media. Increasing amounts of
sensor and machine-to-machine data being
collected. In the past, excessive data volume was
a storage issue. But with decreasing storage
costs, other issues emerge, including how to
determine relevance within large data volumes
and how to use analytics to create value from
relevant data.

9. The FOUR V’s of Big Data

10. The FOUR V’s of Big Data
Variety. Data today comes in all types of formats.
Structured, numeric data in traditional databases.
Information created from line-of-business
applications. Unstructured text documents,
email, video, audio, stock ticker data and
financial transactions. Managing, merging and
governing different varieties of data is something
many organizations still grapple with.

11. The FOUR V’s of Big Data

12. The FOUR V’s of Big Data
Velocity. Data is streaming in at unprecedented
speed and must be dealt with in a timely manner.
RFID tags, sensors and smart metering are driving
the need to deal with torrents of data in near-
real time. Reacting quickly enough to deal with
data velocity is a challenge for most
organizations.

13. The FOUR V’s of Big Data

14. The FOUR V’s of Big Data
Veracity - Big Data Veracity refers to the biases,
noise and abnormality in data. Is the data that is
being stored, and mined meaningful to the
problem being analyzed. Inderpal feel veracity in
data analysis is the biggest challenge when
compares to things like volume and velocity. In
scoping out your big data strategy you need to
have your team and partners work to help keep
your data clean and processes to keep ‘dirty data’
from accumulating in your systems.

15. Who’s Generating Big Data
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time)
Sensor technology and networks
(measuring all kinds of data)
• The progress and innovation is no longer hindered by the ability to collect data
• But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable fashion
15

16. The importance of Big Data
The real issue is not that you are acquiring large
amounts of data. It's what you do with the data that
counts. The hopeful vision is that organizations will
be able to take data from any source, harness
relevant data and analyze it to find answers that
enable:
• Cost reductions
• Time reductions
• New product development and optimized offerings
• Smarter business decision making

18. The importance of Big Data
For instance, by combining big data and high-powered analytics, it is possible
to:
• Determine root causes of failures, issues and defects in near-real time,
potentially saving billions of dollars annually.
• Optimize routes for many thousands of package delivery vehicles while
they are on the road.
• Analyze millions of SKUs to determine prices that maximize profit and
clear inventory.
• Generate retail coupons at the point of sale based on the customer's
current and past purchases.
• Send tailored recommendations to mobile devices while customers are in
the right area to take advantage of offers.
• Recalculate entire risk portfolios in minutes.
• Quickly identify customers who matter the most.
• Use clickstream analysis and data mining to detect fraudulent behavior

19. HDFS / Hadoop
Data in a HDFS cluster is broken down into
smaller pieces (called blocks) and
distributed throughout the cluster. In this
way, the map and reduce functions can
be executed on smaller subsets of your
larger data sets, and this provides the
scalability that is needed for big data
processing. The goal of Hadoop is to use
commonly available servers in a very
large cluster, where each server has a set
of inexpensive internal disk drives.

20. PROS OF HDFS
• Scalable – New nodes can be added as needed,
and added without needing to change data
formats, how data is loaded, how jobs are
written, or the applications on top.
• Cost effective – Hadoop brings massively parallel
computing to commodity servers. The result is a
sizeable decrease in the cost per terabyte of
storage, which in turn makes it affordable to
model all your data.
• Flexible – Hadoop is schema-less, and can absorb
any type of data, structured or not, from any

24. Sources
• McKinsey Global Institute
• Cisco
• Gartner
• EMC, SAS
• IBM
• MEPTEC

25. Thank you for your
attention.
Authors: Tomasz Wis
Krzysztof Rudnicki