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Submitted By
J. Subha, M.Tech II Year
M.S. University, Tirunelveli.
 Introduction Big Data
 Data Facts
 Characteristics of Big Data
 Type of Data
 Big Data Tools
 Hadoop
No single definition: here is from Wikipedia:
 Big data is 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.
 Involves various tools, techniques and
frameworks.

Customer
Social
Media
Gamin
g
Entertai
n
Bankin
g
Financ
e
Our
Know
n
Histor
y
Purcha
se
 Over 90% of all the data in the world was
created in the past 2 years.
 Every 2 days we create as much information.
 The total amount of data being captured and
stored by industry doubles every years.
 Every minute we send 204 million emails,
Generate 1.8 million Facebook likes, send
278 thousand Tweets, and upload 200,000
photos to Facebook
 Around 100 hours of video are uploaded to
every minute.
 Big data (TB) cannot fit in a memory of single
computer
 RDBMS fail to handle Big Data
 Processing of Big data in a single computer
will take a lot of time.
 Big data cannot be analyzed with a traditional
tools.
 Characteristics of Big Data:5V’s
 Volume – Data Quantity
 Velocity – Data Speed
 Variety - Data Types
 Veracity – Data Quality and accuracy
 Value - Data Value
 Turning Big Data into Value: The latest
technology such as Distributed systems and
cloud computing together with the latest
software and analysis approaches allow us to
leverage all types of data to gain insights and
add value.
The Model of Generating/Consuming Data has Changed
Old Model: Few companies are generating data, all others are
consuming data
New Model: all of us are generating data, and all of us are
consuming data
Processing Big Data
 Unstructured - Video data, audio data,
( PDF)
 Semi-structured - Many sources of big data
( XML)
 Structured - Most traditional data sources
(Tables)
 Sensors
 Cc-cams
 Social Network- FB..
 Online Shopping
 Airlines
 Hospitality data etc.,
 Big Data is needed – Increase of storage
capacities – Increase of processing power –
Availability of data (different data types).
 Collecting
 Organizing
 Analyzing of Large
set of data to discover
pattern or other
useful information.
Organizing
Analyzing
Collecting
Representation
 Hadoop – Getting huge data, processed in
less time
 Storing and processing huge amount of data
 Hadoop is the Open source frame work
software, that is developed by ‘Apache’ to
support distributed processing of data.
 Initially, Java Language was used to develop
Hadoop script, but today many other
languages are used for scripting Hadoop.
 Hadoop is used to helps in data analytics
 Hadoop implements Google’s MapReduce,
using HDFS
 MapReduce divides applications into many
small blocks of work.
 HDFS creates multiple replicas of data
blocks for reliability, placing them on
compute nodes around the cluster.
 MapReduce can then process the data
where it is located.
 Hadoop ‘s target is to run on clusters of the
order of 10,000-nodes.
 Hardware Requirements
 Quad core processor- 64 bit
 RAM – 8GB
 Disk Free – 20 GB
 Software Requirements
 Windows 7+, MAC Osx10.10+,..
 Several Opensource Software tools including
Apache Hadoop.
Thank You,

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Big Data

  • 1.
  • 2.
  • 3. Submitted By J. Subha, M.Tech II Year M.S. University, Tirunelveli.
  • 4.  Introduction Big Data  Data Facts  Characteristics of Big Data  Type of Data  Big Data Tools  Hadoop
  • 5. No single definition: here is from Wikipedia:  Big data is 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.  Involves various tools, techniques and frameworks.
  • 7.
  • 8.  Over 90% of all the data in the world was created in the past 2 years.  Every 2 days we create as much information.  The total amount of data being captured and stored by industry doubles every years.  Every minute we send 204 million emails, Generate 1.8 million Facebook likes, send 278 thousand Tweets, and upload 200,000 photos to Facebook  Around 100 hours of video are uploaded to every minute.
  • 9.  Big data (TB) cannot fit in a memory of single computer  RDBMS fail to handle Big Data  Processing of Big data in a single computer will take a lot of time.  Big data cannot be analyzed with a traditional tools.
  • 10.  Characteristics of Big Data:5V’s  Volume – Data Quantity  Velocity – Data Speed  Variety - Data Types  Veracity – Data Quality and accuracy  Value - Data Value  Turning Big Data into Value: The latest technology such as Distributed systems and cloud computing together with the latest software and analysis approaches allow us to leverage all types of data to gain insights and add value.
  • 11.
  • 12.
  • 13. The Model of Generating/Consuming Data has Changed Old Model: Few companies are generating data, all others are consuming data New Model: all of us are generating data, and all of us are consuming data
  • 14. Processing Big Data  Unstructured - Video data, audio data, ( PDF)  Semi-structured - Many sources of big data ( XML)  Structured - Most traditional data sources (Tables)
  • 15.  Sensors  Cc-cams  Social Network- FB..  Online Shopping  Airlines  Hospitality data etc.,  Big Data is needed – Increase of storage capacities – Increase of processing power – Availability of data (different data types).
  • 16.  Collecting  Organizing  Analyzing of Large set of data to discover pattern or other useful information. Organizing Analyzing Collecting Representation
  • 17.
  • 18.  Hadoop – Getting huge data, processed in less time  Storing and processing huge amount of data  Hadoop is the Open source frame work software, that is developed by ‘Apache’ to support distributed processing of data.  Initially, Java Language was used to develop Hadoop script, but today many other languages are used for scripting Hadoop.  Hadoop is used to helps in data analytics
  • 19.
  • 20.  Hadoop implements Google’s MapReduce, using HDFS  MapReduce divides applications into many small blocks of work.  HDFS creates multiple replicas of data blocks for reliability, placing them on compute nodes around the cluster.  MapReduce can then process the data where it is located.  Hadoop ‘s target is to run on clusters of the order of 10,000-nodes.
  • 21.  Hardware Requirements  Quad core processor- 64 bit  RAM – 8GB  Disk Free – 20 GB  Software Requirements  Windows 7+, MAC Osx10.10+,..  Several Opensource Software tools including Apache Hadoop.

Notes de l'éditeur

  1. B