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•Volume
•Velocity
•Veracity
•Variety
•Value
•MEGA BYTE
•GIGA BYTE
•TERA BYTE
•PETA BYTE
•EXTRA BYTE
•ZETTA BYTE
•Collect
•Discover
•Process
•Manage
•Generate
•How to handle Heterogeneity
•Scalability (volume)
•Structured / Semi structured/
Unstructured data handling
•Timeliness r...
•Development of analytics
•Design & develop appropriate data
models
•Developments of tools
•Creating Data warehouse & Mini...
•Variety of Formats
•Ambiguity of Data
•Abstracted layer of Hierarchy
•Lack of Meta Data
•Finding patterns
• Deriving meaning
• Making decisions
• Responding to the world with intelligence
•Lexical Analysis
•Syntactic Analysis
•Semantic Analysis
•Sentimental Analysis
•Big Table
•Business Intelligence
•Cassandra
•Hadoop and MapReduce
•R
•Deals with extracting information
from data
•Use this information to predict
outcomes
•Find patterns that predict similar...
•Hadoop and Mapreduce
•Integrating R and Hadoop
•Tools like RHIPE and RHadoop
By combining capability of Hadoop
and R by using Rhadoop tool the
performance and accuracy of data
analytics can be increa...
THANKS
Queries ?
Big data
Big data
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Big data

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It contains overview about Big data and also contain some details regarding Big data

Published in: Technology
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Transcript of "Big data"

  1. 1. •Volume •Velocity •Veracity •Variety •Value
  2. 2. •MEGA BYTE •GIGA BYTE •TERA BYTE •PETA BYTE •EXTRA BYTE •ZETTA BYTE
  3. 3. •Collect •Discover •Process •Manage •Generate
  4. 4. •How to handle Heterogeneity •Scalability (volume) •Structured / Semi structured/ Unstructured data handling •Timeliness response •Handle privacy and security issues
  5. 5. •Development of analytics •Design & develop appropriate data models •Developments of tools •Creating Data warehouse & Mining techniques •Business intelligence •Storage consideration
  6. 6. •Variety of Formats •Ambiguity of Data •Abstracted layer of Hierarchy •Lack of Meta Data
  7. 7. •Finding patterns • Deriving meaning • Making decisions • Responding to the world with intelligence
  8. 8. •Lexical Analysis •Syntactic Analysis •Semantic Analysis •Sentimental Analysis
  9. 9. •Big Table •Business Intelligence •Cassandra •Hadoop and MapReduce •R
  10. 10. •Deals with extracting information from data •Use this information to predict outcomes •Find patterns that predict similar outcome in future
  11. 11. •Hadoop and Mapreduce •Integrating R and Hadoop •Tools like RHIPE and RHadoop
  12. 12. By combining capability of Hadoop and R by using Rhadoop tool the performance and accuracy of data analytics can be increased
  13. 13. THANKS
  14. 14. Queries ?
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