This document discusses how Apache Hadoop enables large amounts of structured and unstructured data to be processed across clusters of computers. It explains that Hadoop provides high availability and allows for 100% utilization of RAM through its distributed architecture and MapReduce programming model. Examples of industries using Hadoop include financial services, retail, telecom, manufacturing, healthcare, and public sector.

2. Breaking points of traditional approach

3. Monitor

4. How Business Get Hike Though This Items

5. BI(Business Intelligence)

6. Industry use cases of Hadoop
Financial services Retail Telecom Manufacturing
Healthcare Public sectorUtilities, oil and gas

7. Google Analysis On Traffic

8. What if you could handle big data?

9. Website Generate Data
 E- commerce website e.g. flipkart, amazon
 Social Network website
 Aeroplan Black box
 NSE Stock Exchange

10. Hadoop Technology
Hadoop is changing the perception of handling Big Data especially the unstructured
data. Let’s know how Apache Hadoop software library, which is a framework, plays a vital
role in handling Big Data. Apache Hadoop enables surplus data to be streamlined for
any distributed processing system across clusters of computers using simple
programming models. It truly is made to scale up from single servers to a large number
of machines, each and every offering local computation, and storage space. Instead of
depending on hardware to provide high-availability, the library itself is built to detect and
handle breakdowns at the application layer, so providing an extremely available service
along with a cluster of computers, as both versions might be vulnerable to failures.

11. History of hadoop

12. Why hadoop
 Open Source (software available at free of cost)
 100% utilization of Ram
 Fast Processing through Map Reduce
 Data Storage through HDFS(Hadoop File system)
 Cheap commodity , no need to buy extra hardware for PC
 Hadoop can be implemented on Cloud

13. Growth rate of hadoop

15. Data volume
Hadoop stores files in a distributed file system
Hadoop can store very large amounts of data

16. Data variety
Hadoop stores files (non-relational store)

17. Data velocity
Hadoop can stream live data and process them in real-time

18. Main Component of hadoop