Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.

1. Hadoop Basic Concepts
Marian Faryna

2. Buzzword Bingo
BigData Ecosystem HDFS Replica
factor
Data locality Hive Data variety Column-based
storage
Commodity
hardware
Resource
manager
MapReduce High
Availability
Coordination
service Shuffling
Eventual
consistency
Name Node

3. WHY HADOOP?

4. PROBLEMS
WITH
RELATIONAL
DATABASES

5. HADOOP CAN:
● Process large data sets effectively
● Work with structured/unstructured data
● Process data in different modes

6. ADDITIONAL HADOOP PROS
● High Availability
● Horizontal Scalability
● Commodity Hardware
● BASE Principle

7. Hadoop BASE Principle :
● Basically Available
● Soft state
● Eventually consistent

8. 100 nodes cluster. 800 scrobblers per second. 40 million per day
Hadoop cluster consist of 532 nodes, 120 million active users,
300+ million search queries daily
1100 nodes, processing 12 PB storage data. 200+ million active users.
30 million users update their statuses at least once each day
1650 nodes cluster 75+ millions of active users, 30+ million
songs 1+ billion plays per day
Who is using Hadoop

9. WHAT IS HADOOP?

10. Hadoop is an ecosystem for
distributed processing and
distributed storage large sets
of data

11. HADOOP
Ecosystem
column-basedstorage
coordination
service

13. New Node
Journal Node
Basically Available
Highly Available
replica factor
HDFS

14. Eventual Consistency
BASE Principle

15. Soft State
BASE Principle

16. MAPREDUCE

17. MapReduce algorithm

18. MapReduce example

19. Altogether
data locality
resource manager

20. IT’S ALL ABOUT DATA
processing
And remember

21. Thanks!
Any questions?