Introduction Hadoop: Introduction Hadoop: Architecture Hadoop:Map-reduce DFS and HDFS Summary

2. Session Objectives
Introduction
Hadoop: Introduction
Hadoop: Architecture
Hadoop:Map-reduce
DFS and HDFS
Summary

3. Introduction
Big Data:?
Challenges of BIG DATA
Big Data Sources
Use cases of Big
Big data characteristics :
• Volume : Extremely large amount of data
• Velocity: Rate at which the data is generated
• Variety: Structured, semi-structured, unstructured data,
Big data Technologies:

4. Hadoop : Introduction
Hadoop. It allows the distributed processing on large
clusters and datasets
It’s a data management technology or a framework
Why Hadoop?
• Scalable.
• Cost effective.
• Fast,
• Resilient to failure.

5. Hadoop : Cluster
Master with 5 slave , 50TB data for next 5 months

6. Hadoop : Cluster

7. Hadoop : Components

8. Hadoop1 : Daemons

9. Hadoop2 : Daemons
1. Name Node
2. Data node
3. Secondary name node
4. Resource manager :
5. Node Manager

10. Hadoop : Master Slave Architecture
Hadoop
HDFS
Name Node Data Node
MR/YARN
Resource Manager
Node Manager
Master
Slave

11. Hadoop : Master Slave Architecture

12. Hadoop : Modes of Operations

13. Hadoop1 vs Hadoop2

14. Hadoop1 Limitations
Single point failure
Low resource utilization
Less scalable as compare to Hadoop2

15. Ecosystem

16. DFS and HDFS

17. DFS and HDFS

18. HDFS Read Operation

19. HDFS Write Operation

20. File block and replication

21. Thank You