1) Airlines and stock exchanges generate large amounts of data, with airlines collecting 10 terabytes per 30 minutes of flight time and the NYSE generating 1 terabyte of trade data daily. 2) Big data refers to a firm's ability to store, process, and access large amounts of data to make effective decisions and serve customers. What constitutes "big data" can be measured in bytes, kilobytes, megabytes, terabytes and larger units. 3) Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computer servers using simple programming models. It allows for the distributed processing of large datasets in a reliable, fault-tolerant manner.

1. IN THE NAME OF GOD
BIG DATA ANALYTICS
HADOOP AND CASSANDRA
Author: Samira Riki

2. A airline jet collect 10 terabytes of sensor data
for every 30 minutes of flying time.
NYSE generates about one terabyte of new trade
data per day to perform stock trading analytics to
determine trends for optimal trades.

3. 3
 Twitter has over 500 milion registered users.
 79% of US Twitter users are more likely to buy from brands
they follow.
 67% of US Twitter users are more likely to buy from brands
they follow.
 57% of all companies that use social media for business use
Twitter.

4. “Big Data is the frontier of a firm's ability to
store, process, and access (SPA) all the data
it needs to operate effectively, make
decisions, reduce risks, and serve
customers.”

6. ... How big is BIG?

7. Let’s look at
Big Data
in a different way…

8. Byte
Byte : one grain of rice

9. Kilobyte
Byte : one grain of rice
Kilobyte : cup of rice

10. Megabyte
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice

11. Gigabyte
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks

12. Terabyte
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships

13. Petabyte
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan

14. One ByteExabyte
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states

15. Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Zettabyte

16. Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL! Yottabyte

17. HobbyistByte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

18. Desktop
HobbyistByte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

19. Desktop
Hobbyist
Internet
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

20. Desktop
Hobbyist
Internet
Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

21. Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

22. Desktop
Hobbyist
The Future?
Internet
Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets west coast states
Zettabyte : Fills the Pacific Ocean
Yottabyte : A EARTH SIZE RICE BALL!

23. Process data in parallel? -not simple 
23
 An idea: parallelism
 A problem: Parallelism is Hard
Synchronization
Deadlock
Limited bandwidth
Timing issues and co-ordination
Split and Aggregation
 Coputer are complicate
Driver failure
Data availability
Hey! We have Distributed computing!!!

24. Yes,we have distributed computing and it also come up with
some challenges
24
 Resource sharing
 Concurrency
 Fault tolerance
 Heterogeneity
 Transparency
To address most of these challenges(but not all) Hadoop
come in.

25. Hadoop origin
25
• An Elephant can’t jump.But can carry heavy load!!!
• Apache Haddop is a framework that allows for the distributed
processing of large data sets across clusters of commodity
computers using a simple programming model.it is designed to scale
up from single servers to thousands of machines,each providing
computation and storage.
• Hadoop is an open-source implementation of Google
MapReduce,GFS(distributed file system).
• Hadoop was created by Doug Cutting the creator of Apache
Lucene,the widely used text search library.

26. Hadoop Architecture
26
Hadoop designed and built on two independent frame works.
Hadoop= HDFS + Map reduce
HDFS(Storage and File system):HDFS is a reliable distributed file system
that provides high-throughput access to data.
MapReduce(processing):MapReduce is a framework for performing high
performance distributed data processing using the divide and aggregate
programming paradigm.
Hadoop has a master/slave architecture for both storage and
processing.

27. Hadoop Master and Slave Architecture
27
The components of HDFS are
 Name Node
 Data Node
 Secondary Name Node

28. 28

29. 29

30. 30
The components of MapRedeuce are:
 Job Tracker
 Task Trackers

31. Who uses Hadoop?
31
 Amazon/A9
 Facebook
 Google
 IBM
 Joost
 Last.fm
 New York Times
 PowerSet
 Yahoo!
 Twitter
 LinkedIn

32. Cassandra
32
• Apache Cassandra is an open source distributed database
management system designed to handle large amounts of data
across many commodity servers, providing high availability with no
single point of failure. Cassandra offers robust support for clusters
spanning multiple datacenters.

33. Main features
33
 Cassandra places a high value on performance.
In 2012, University of Toronto researchers studying NoSQL systems
concluded that "In terms of scalability, there is a clear winner
throughout our experiments.
 Decentralized
 Supports replication and multi data center replication
 Scalability
 Fault-tolerant
 Query language
 MapReduce support

34. The data model
34

35. New use cases
35
• Geographic data
• Weather data
• Rfid
• Travel schedules
• Hotel reservation

36. Big Data isn’t big,
if you know how to
use it.

37. References
37
1.Big data:the next frontier for innovation,competition
and productivity-McKinsy&company
2. Big Data Meets Big Data Analytics-SAS Company
3. Big data tutorial-Marko Grobelnik
4. Big Data Spectrum

38. 38
Q?