Information about Data Analytics and Big Data.

1. Data Analytics – All about data
Harish Dixit

2. Getting the context
• Examining raw data.
• Discovering useful information.
• Make better business decisions.

3. Life cycle stages

4. What is Big Data?

5. From where data is coming?

6. Complex ecosystem
Cloud

7. Hadoop Architecture

8. HDFS
• Distributed file system.
• Partitioning of data.
• Fault tolerant.
• Java API.
• Higher scalability.
• Master slave paradigm.

9. HDFS

10. Map Reduce
• Parallel processing model.
• Move operations not data.
• Distributed computations.
• User defined functions
– Map()
– Reduce()

11. Map/Reduce Operations

12. Example

13. R - Add analytic power to Hadoop

14. Data modeling techniques
• Regression
• Classification
• Clustering
• Recommendation
• Text mining

15. Regression
Regression can be formulated as follows:
y = ax +e
x y
-----------------------------
63 3.1
64 3.6
65 3.8
66 4
-----------------------------

16. Classification

17. Clustering

18. Recommendation

19. Text Mining

20. NoSQL
• Non-relational.
• Distributed environment.
• Large volume.
• No fixed schemas.
• Horizontally scalable.

21. CAP Theorem (any 2 of 3)

22. Variants of NoSQL
• Key-Value Systems.
• Document-based Systems.
• Column-based Systems.
• Graph-based Systems.

23. Distributed Key-Value Systems

24. Column-based Systems

25. Applications of Big Data

26. Think Big
Q & A