The document discusses big data concepts including volume, velocity, variety and value of data. It covers challenges in big data such as handling different data types, scalability, and privacy issues. It also discusses tools and techniques for big data analytics including Hadoop, MapReduce, and integrating R and Hadoop for more accurate analytics and increased performance.

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 ?