This document compares and contrasts data warehouses and big data. It discusses how big data has evolved from data warehousing technologies and involves new technologies like Hadoop and MapReduce. While data warehouses ensure consistent decision making using prior hypotheses, big data uses statistics to extract new hypotheses from very large and diverse datasets, including clickstream logs, sensor data, social media, and more. Both hard and soft data sources are important for businesses to analyze and extract value from immense and growing amounts of information.

1. vs
Big Data
Big Data
Big Data
Lisette Zounon , CC, ALB
Advanced Manual : Technical Presentations
Project #4 : Presenting a Technical Paper
Time : 10-12 minutes

2. Deliver business value through the
analysis of data
Data Warehouse or Big Data

3. A taxonomy of data
mind over matter

4. Matter Data
• Data from physical world .
• Measurement data : sourced
from various sensors connected
to computers and internet .
• Atomic data : comprised of
physical events specific to
human interactions.
• Derived data: from mathematical
manipulation of atomic data ,
create for meaningful view of
business information to humans.

5. Mind Data
• Soft information originating from
the way , humans perceive the
world and interact socially within
it .
• Multiplex data : image , video
and audio information , very
large files .
• Textual data : suited for
statistical analysis
• Compounded data : hard and
soft information , metadata is a
significant subset . social media
information

6. Data Warehouse
• Originated from relational
database
• Data are sourced solely from
other databases within the
organization
• Financial systems , customer
informations and billing
informations etc..

7. Relational Database

8. Analytic database
Better performance , faster query , efficient reporting.

10. Data sizes constantly moving target, as
big as you want , getting ever larger.
Allow business to analyze and extract business value
from the immense data sets .

11. clickstream logs , sensor data , location data
,customer support emails , chat
transcripts ,surveillance video, etc..

12. Big data components
• Big data is in many ways
evolution of data warehousing.
• New technologies : Hadoop ,
MapReduce , NoSQL queries
or databases .

14. Data warehouse Vs Big data
• Data warehouse : ensure
consistent and trusted
decision making
• Business intelligence works
more from prior hypotheses ,
whereas big data uses
statistics to extract hypotheses
.

15. Data warehouse vs Big data
• Single logical storehouse
• Significant overlap in the world
of matter : Meaning
• Big Data is the superset of
information and processes that
have characterized data
warehousing .

16. Conclusion
Data to make effective
decision making
Consistent and trusted
information
Hard and soft information
highly complimentary and
mandatory for all forward
looking businesses.

17. • References
• http://strata.oreilly.com/2011/01/data-warehouse-
big-data.html
• oracle database 12c for Data warehousing and
Big Data

18. – Criss Jami, Venus in Arms
“In the age of technology there is constant
access to vast amounts of information. The
basket overflows; people get overwhelmed;
the eye of the storm is not so much what goes
on in the world, it is the confusion of how to
think, feel, digest, and react to what goes on."

19. Thank you .
end