seminor of BIG DATA

1. Presented by
B Srujana
MTECH(CSE)
19K91D5813

2. Contents
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing ,selecting and processing of Big Data
5. Big Data Examples
6.Tools used in Big Data
7.Application of Big Data
8.Future of Big Data
9.Conclusion
10.Referrences

3. 1.Introduction
• Big Data may well be the Next Big Thing in the IT world.
• 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 Face book were
built around big data from the beginning.
• Like many new information technologies, big data can bring
about dramatic cost reductions, substantial improvements in
the time required to perform a computing task, or new
product and service offerings.

4. What is Data?
The quantities, characters, or symbols on which operations are
performed by a computer, which may be stored and
transmitted in the form of electrical signals and recorded on
magnetic, optical, or mechanical recording media.

5. 2. WHAT is BIG DATA
Big Data is also data but with a huge size. Big Data
is a term used to describe a collection of data that is
huge in size and yet growing exponentially with
time. In short such data is so large and complex that
none of the traditional data management tools are
able to store it or process it efficiently

6. 3. Characteristic of Big Data
Volume – The name Big Data itself is related to a size which is enormous. Size of data
plays a very crucial role in determining value out of data. Also, whether a particular data can
actually be considered as a Big Data or not, is dependent upon the volume of data.
Hence, 'Volume' is one characteristic which needs to be considered while dealing with Big Data
 Variety – The next aspect of Big Data is its variety.
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured.
During earlier days, spreadsheets and databases were the only sources of data considered by most
of the applications. Nowadays, data in the form of emails, photos, videos, monitoring devices,
PDFs, audio, etc. are also being considered in the analysis applications. This variety of
unstructured data poses certain issues for storage, mining and analyzing data.
 Velocity – The term 'velocity' refers to the speed of generation of data. How fast the
data is generated and processed to meet the demands, determines real potential in the
data. Big Data Velocity deals with the speed at which data flows in from sources like
business processes, application logs, networks, and social media sites, sensors, Mobile
devices, etc. The flow of data is massive and continuous

7. Variability – This refers to the inconsistency which can be shown by the data at times,
thus hampering the process of being able to handle and manage the data effectively

8. 4.Storing ,Selecting and Processing of Big Data
1.Storing
Analyzing your data characteristics
• Selecting data sources for analysis
• Eliminating redundant data
• Establishing the role of No SQL
Overview of Big Data stores
• Data models: key value, graph, document, column-family
• Hadoop Distributed File System
• H Base
• Hive
2.Selecting
•Choosing the correct data stores based on your data characteristics
• Moving code to data and Implementing polyglot data store
solutions
• Aligning business goals to the appropriate data store

9. 3.STORING OF BIGDATA
Integrating disparate data stores
• Mapping data to the programming framework
• Connecting and extracting data from storage
• Transforming data for processing
• Subdividing data in preparation for Hadoop Map Reduce
Employing Hadoop Map Reduce
• Creating the components of Hadoop Map Reduce jobs
• Distributing data processing across server farms
• Executing Hadoop Map Reduce jobs
• Monitoring the progress of job flows

10. The Structure of Big Data
Structured
Any data that can be stored, accessed and processed in the
form of fixed format is termed as a 'structured' data.
Unstructured
Any data with unknown form or the structure is classified
as unstructured data. In addition to the size being huge,
un-structured data poses multiple challenges in terms of
its processing for deriving value out of it.
Semi-structured
Semi-structured data can contain both the forms of data.
We can see semi-structured data as a structured in form
but it is actually not defined with e.g. a table definition in
relational DBMS. Example :an XML file

12. 5.Examples of Big Data
New York Stock Exchange:
The New York Stock Exchange generates about one
terabyte of new trade data per day.
Social Media:
The statistic shows that 500+terabytes of new data get
ingested into the databases of social media
site Facebook, every day. This data is mainly
generated in terms of photo and video uploads,
message exchanges, putting comments etc.
Jet Engine:
A single Jet engine can generate 10+terabytes of data
in 30 minutes of flight time. With many thousand
flights per day, generation of data reaches up to
many Petabytes

13. 6.Types of top tools used in Big-Data
Hadoop. Apache Apache Spark Apache Storm.
Cassandra. RapidMiner.MongoDB.
R Programming Tool. Neo4j.

14. Maximilien Brice, © CERN

16. 7.Application Of Big Data analytics
•Homeland Security
• Smarter Healthcare
•Multi-channel sales
•Telecom
•Manufacturing
• Traffic Control
•Trading Analytics
• Search Quality

17. 8.Future of Big Data
• $15 billion on software firms only specializing in data
management and analytics.
• This industry on its own is worth more than $100 billion and
growing at almost 10% a year which is roughly twice as fast as
the software business as a whole.
• In February 2012, the open source analyst firm Wikibon
released the first market forecast for Big Data , listing $5.1B
revenue in 2012 with growth to $53.4B in 2017
•The McKinsey Global Institute estimates that data volume is
growing 40% per year, and will grow 44x between 2009 and
2020.