2. BIG DATA

3. BIGDATA, collection of large and complex data sets difficult to process using on-hand database tools

5. V V V V V V

6. WHY IS IT INTRODUCED  Lots of data is being collected & warehoused.  Processing exceeds database system capacity.  Structured & Unstructured data  Separation of data from application.  Understanding data analytics.  Faster development, faster runtime.  Elastic Feature-Level Scalability.

7. APACHE HADOOP APACHE HADOOP  Provides massive scalable storage, its not a database  Data Processing Platform  HDFS, a fault tolerant storage  Store data in native format  Reduce cost & lower risks  Extracting business value from data  Deliver new insights  Automatically handles s/w & h/w failures

8. HDFS  Fault tolerant storage  Survive failure on disk, network and network interface  Uses Map-Reduce programs  Creates clusters of machines and co-ordinates  Storage on clusters using blocks  No special hardware compared to RAID

10. PROBLEMS WITH BIG DATA PROBLEMS WITH BIG DATA  Will be so overwhelmed  Costs escalate too fast  Storage consumed 3 times  Timeliness  Analysis  Poor data locality  Incompatible & Replicated data

11. CONCLUSION  Big Data will replace the approaches, tools and systems that underpin development work.  Better analysis of the large volumes of data.  Potential for advancing in many scientific disciplines.  Improving the profitability.  Technical challenges to be addressed dynamically

12. REFERENCES  www.bigdatauniversity.com www.bigdatauniversity.com  www.sas.com/big-data/ www.sas.com/big-data/  en.wikipedia.org/wiki/Big_data en.wikipedia.org/wiki/Big_data  cra.org/ccc/docs/init/bigdatawhitepaper.pdf cra.org/ccc/docs/init/bigdatawhitepaper.pdf  dataanalyticssummit.com dataanalyticssummit.com  hadoop.apache.org hadoop.apache.org

13. THANK YOU Today’s Big Data Is Not Tomorrow’s Big Data Tomorrow’s Big Data