1. B ig D ata Analysis for Page Ranking using Map/Reduce R.Renuka, R.Vidhya Priya, III B.Sc., IT, The S.F.R.College for Women, Sivakasi.

2. Overview Introduction What is Big Data! Why Big Data? 4 V’s Of Big Data Big Data Analytics Technologies Map/Reduce Applications Case Study Conclusion

3. Introduction Data have outgrown the storage and processing capabilities of a single host. Two fundamental challenges: –how to store and –how to work with voluminous data sizes, and, –how to understand data and turn it into a competitive advantage.

4. What is Big Data! ‘Big-data’ is similar to ‘Small-data’, but bigger But having data bigger requires different approaches: techniques, tools & architectures To solve: New problems and old problems in a better way.

5. The Blind men and the Elephant

6. Why Big Data? Key enablers for the growth of “Big Data” are: Increase of Processing Power Increase of Storage Capacities Availability of Data

7. 4 V’s of Big Data

8. Big Data Analytics Technologies Hadoop PLATFORA WibiData PIG Hive MapReduce NoSQL databases Column-oriented databases

9. Hadoop Hadoop is a distributed file system and data processing engine Hadoop has two components: –The Hadoop distributed file system (HDFS) –The MapReduce programing.

10. Map / Reduce A High level abstracted framework for distributed processing of large datasets Fault Tolerant, Parallelization Computation consists of two phases Map Reduce A Master-Slave architecture Computations occurs in multiple slave nodes And it tries to provide data locality as much as possible.

11. MR model Map –Process a key/value pair to generate intermediate key/value pairs Reduce –Merge all intermediate values associated with the same key Users implement interface of two primary methods: 1. Map: (key1, val1) → (key2, val2) 2. Reduce: (key2, [val2]) → [val3]

12. Applications

13. Homeland Security FinanceSmarter Healthcare Multi-channel sales Telecom Manufacturing Traffic Control Trading AnalyticsFraud and Risk Log Analysis Search Quality Retails

14. Case Study

16. Conclusion Real-time big data isn’t just a process for storing petabytes or exabytes of data in a data warehouse, It’s about the ability to make better decisions and take meaningful actions at the right time.

17. Queries ??