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Big data presenation
1.
2.
3.
•Volume •Velocity •Veracity
•Variety •Value
4.
•MEGA BYTE •GIGA
BYTE •TERA BYTE •PETA BYTE •EXTRA BYTE •ZETTA BYTE
5.
•Collect •Discover •Process
•Manage •Generate
6.
•How to handle
Heterogeneity •Scalability (volume) •Structured / Semi structured/ Unstructured data handling •Timeliness response •Handle privacy and security issues
7.
•Development of analytics
•Design & develop appropriate data models •Developments of tools •Creating Data warehouse & Mining techniques •Business intelligence •Storage consideration
8.
•Variety of Formats
•Ambiguity of Data •Abstracted layer of Hierarchy •Lack of Meta Data
9.
•Finding patterns •
Deriving meaning • Making decisions • Responding to the world with intelligence
10.
•Lexical Analysis •Syntactic
Analysis •Semantic Analysis •Sentimental Analysis
11.
•Big Table •Business
Intelligence •Cassandra •Hadoop and MapReduce •R
12.
•Deals with extracting
information from data •Use this information to predict outcomes •Find patterns that predict similar outcome in future
13.
•Hadoop and Mapreduce
•Integrating R and Hadoop •Tools like RHIPE and RHadoop
14.
By combining capability
of Hadoop and R by using Rhadoop tool the performance and accuracy of data analytics can be increased
15.
THANKS
16.
Queries ?
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