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1. BigDataBigData
An Introduction byAn Introduction by
KeylabsKeylabs

2. Need For A New Processing Platform
(BigData)
 What is BigData ?
- Twitter (over 7~ TB/day)
- Facebook (over 10~ TB/day)
- Google (over 20~ PB/day)
 Where does it come from ?
 Existing systems (vertical scalibility)
 Why Hadoop (horizontal scalibility)?

3. Origin of Hadoop

4. Companies Using Hadoop
 Yahoo
 Google
 Facebook
 LinkedIn
 IBM
 Amazon
 HortonWorks
 Cloudera
 NY Times
 … the list goes on.

5. What is Hadoop?
 Flexible infrastructure for large scale computation & data
processing on a network of commodity hardware.
 Completely written in java.
 Open source & distributed under Apache license
 Hadoop Core Components: HDFS & MapReduce.
 The Apache Hadoop software library is a
framework that allows for the distributed
processing of large data sets across clusters of
computers using simple programming models.

6. What Hadoop is Not?
 A File system
 A database
 An online transaction processing (OLTP) system
 Replacement of all programming logic

7. Three Vs of Hadoop and
counting…

8. Hadoop Introduction and Architecture

9. Hadoop High-Level Architecture

10. Hadoop Architecture
Admin Node
Job Tracker
Name Node
Task Tracker
Data Node
Task Tracker
Data Node
Task Tracker
Data Node
MapReduce
Engine
HDFS
Cluster

11. Hadoop Cluster

12. Distributed File System
Hadoop Distributed File System
Read 1TB Data
1 Machine
•4 I/O Channels
•Each Channel – 100MB/s
10 Machines
•4 I/O Channels
•Each Channel – 100MB/s

13. What’s so Special About Open Source
Hadoop?

14. HDFS - Hadoop Distributed File
System
 Design of HDFS
 Where HDFS is not a good fit
 Why Is a Block in HDFS So Large?
 Advantage of HDFS?

15. HDFS is not for.
 Low Latency Data Access
 Large number of small files.
 Multiple writers, arbitrary file modifications.

16. HDFS Architecture

17. Let us Zoom into HDFS

18. NameNode
Deeper Things about Name Node
Request to note down these points

19. DataNode
 What is DataNode?

20. NameNode and DataNodes

21. Data Replication
 What is Data Replication

22. Data Replication & Rack
Awareness

23. File Write Operation
 File Write Operation

24. A client writing the data to HDFS

25. File Write Operation in Depth -
1

26. File Write Operation in Depth -
2

27. File Write Operation in Depth -
3

28. File Write Operation in Depth -
4

29. File Write Operation in Depth -
4

30. File Write Operation – Unhappy
Path

31. File Read Operation
 File Read Operation

32. A client reading data from HDFS

33. File Read Operation in Depth -
1

34. File Read Operation in Depth -
2

35. File Read Operation in Depth -
3

36. File Read Operation - Unhappy
Path

37. Secondary NameNode

38. Hadoop Cluster – A Typical
Scenario

39. Hadoop Ecosystem

40. Data Loading Techniques and
Analysis

41. When should we go for Hadoop?
 Data is too huge
 Processes are independent
 Online analytical processing (OLAP)
 Better scalability
 Parallelism
 Unstructured data

42. THANK YOUTHANK YOU
FOR YOURFOR YOUR
ATTENTION!ATTENTION!