This document discusses big data, including its characteristics, advantages, applications, and technologies used to manage it. It defines big data as large volumes of structured and unstructured data that require new technologies to capture, store, and analyze. The "6 V's" of big data are described as volume, velocity, variety, validity, volatility, and veracity. Examples of applications include Netflix using big data for scalable infrastructure, retailers gaining customer insights, and sports teams enhancing performance. Technologies mentioned are NoSQL databases, Hadoop ecosystem, cloud servers and storage, and data processing tools.

1. PRESENTED BY:-
Rutuja Wavare
Ameya Hattarke

2.  Introduction
o What is Big data?
o Characteristics of big data
 Why Big-Data?
 Advantages of big data
 Applications of big data
 Conclusions

4. And many more…

5.  Big data is a buzzword used to describe a
massive volume of both structured and
unstructured data.
 The term may seem to reference the volume
of data, that isn't always the case. when
vendor uses the term Big data it refer to the
technology that an organization requires to
handle the large amounts of data and storage
facilities
 Big data is originated with Web
search companies who needed to query very
large distributed aggregations of loosely-
structured data.

7.  we have all heard of the 3Vs of big data Yet,
Inderpal Bhandar, Chief Data Officer at
Express Scripts noted in his presentation at
the Big Data Innovation Summit in Boston
that there are additional Vs that IT, business
and data scientists need to be concerned
with, Here is an overview the 6V’s of big
data.

9.  Volume-How much data
 Velocity-How fast data is processed
 Variety-The various types of data
 Validity
 Volatility
 Veracity

11. 1. You'll Manage Data Better.
2. You‘ll Benefit From Speed, Capacity and
Scalability of Cloud Storage.
3. End Users Can Visualize Data.
4. Company Can Find New Business
Opportunities.
5. Data Analysis Methods, Capabilities Will
Evolve.

12.  NoSQL Databases-
MongoDB, CouchDB, Cassandra, Redis, BigTable, Hbase,
Hypertable, Voldemort, Riak, ZooKeeper
 MapReduce –
Hadoop, Hive, Pig, Cascading, Cascalog, mrjob, Caffeine, S4,
MapR, Flume, Kafka, Azkaban, Oozie.
 Storage- S3, Hadoop Distributed File System
 Servers -EC2, Google App Engine, Elastic, Beanstalk, Heroku
 Processing-
R, Yahoo! Pipes, Mechanical Turk, ElasticSearch, Datameer,
BigSheets, Tinkerpop

13.  Netflix-Cloud architecture is highly scalable and allows Netflix to
quickly provision computing resources as its sees the need. Traffic
patterns are analyzed across device types and localities to help improve
the reliability of video streaming and plan for growth.
 A 360 degree view of the customer
According to Gallivan. Online retailers want to find out what shoppers are
doing on their sites -- what pages they visit, how long they stay, and when
they leave.
 Improving Sports Performance
 Most elite sports have now embraced big data analytics. We have the IBM Slam
Tracker tool for tennis tournaments; we use video analytics that track the
performance of every player in a football game, and sensor technology in
sports equipment such as basket balls or golf clubs allows us to get feedback
on our game and how to improve it. It helps to track athletes outside of the
sporting environment – using smart technology to track nutrition and sleep. as
well as social media conversations to monitor emotional wellbeing

14. •Big data is a very efficient
way for computing the data it
saves the time .
•Big data is creating very
good opportunity for
Companies to lead in industry