The document discusses MapReduce algorithm for analyzing big data. It defines MapReduce as a programming model and software implementation for processing large datasets in a distributed computing environment. The key aspects covered are: - MapReduce was invented by Jeff Dean and Sanjay Ghemawat at Google as a way to process massive amounts of data in parallel across clusters of computers. - It works by breaking the processing into two phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. - MapReduce provides scalability, flexibility, and fault-tolerance for distributed computing on large datasets and has applications in web analysis, internet security, personalized services and

1. “MapReduce Algorithm To Analysis big data”
D.N.PATEL COLLEGE OF ENGINEERING,
SHAHADA.
P.S.G.V.P.M’S
9/17/2016
presented by:
Humera shaikh

2. MapReduce algorithm to
Analysis big data

3. Objectives

4. What Is MapReduce
Definition:
Programming Model And An Associated implementation
for processing Large Cluster Of data..

5. Google MapReduce
• MapReduce is a concept, method and software for typically batch-based
large-scale parallelization. It is inspired by functional programming's
map() and reduce() functions
• Invented by..
Jeff Dean
Sanjay Ghemawat

6. Sometimes We Have To Deal With Huge Amount Of Data
• TBYTES
• 250
• 200
• 150
• 100
• 50
Before MapReduce
You Facebook Yahoo! German Climate
Computing Centre

7. Architecture
 Input Data
 Map ()
 Reduce()
 Output Data

8. How It Works
• Mapper
“ map(inKey,inValue)->list(outKey, outValue)”
map: (k1 , v1 ) → [(k2 , v2 )]
• Reducer
“reduce(outKey,outValuelist)-> list(finalKey, finalValue)”
reduce: (k2 , [v2 ]) → [(k3 , v3 )]

13. Advantages
• Scalability
• Flexibility
• Fast
• Resilient to failure

14. Disadvantages
• Multiple Data Modification
• Low latency Data Access
• Lots of Small File

15. • Internet security
• Personalized services
• Personalized medicine
Application

16. Future scope
• Map-Reduce-Merge:
Adding merge to the Paradigm allows implementing features of relational algebra
• Hive: Query language for Hadoop
• HBase: Column-oriented distributed database
(modeled after Google’s BigTable)
• Web analysis

17. Conclusion

18. References
[1] Hadoop,“PoweredbyHadoop,”http://wiki.apache.org/hadoop/PoweredBy”.
[2] Hadoop Tutorial,YahooInc ,https://developer.yahoo.com/hadoop/tutorial/index.html
[3] Apache: Apache Hadoop, “http://hadoop.apache.org”
[4] Hadoop Distributed File System (HDFS),” http://hortonworks.com/hadoop/hdfs”

19. Any Question ?

20. Meeting Objectives
• To Some basics before MapReduce
• To Know The History of MapReduce
• To Know What Is MapReduce
• To Know Architecture Of MapReduce
• To Know How It Works
• To Know Application
• To Know Advantages
• To Know Disadvantages
• To Know Future Scope

21. Thank You