3. Big data is the capability to manage a
huge volume of disparate data, at the
right speed, and within the right time
frame to allow real-time analysis and
reaction.
4. Volume : How much data
Velocity : How fast that data
is processed
Variety : The various types of
data
VOLUME
VELOCITY
VARIETY
5. Big Data Warehouse :
A process of transforming data into information and
making it available to users in a timely enough
manner to make a difference
Data had to be gathered from a variety of
relational database sources ,
And then ensured that the metadata was
consistent, and that the data itself was clean and
then well integrated.
6. Data warehouse included the following
characteristics:
It should be organized so that related events are linked
together.
The information should be non-volatile so that it cannot be
inadvertently changed.
Information in the warehouse should include all the applicable
operational sources. The information should be stored in a
way that has consistent definitions and the most up-to-date
values.
7. Big data and data warehousing share the same basic goals : to
deliver business value through the analysis of data.
However, big data and data warehousing differ in the scope of
their data
Big data is in many ways an evolution of data warehousing. To be
sure, there are new technologies used for big data, such as
Hadoop and “nosql” databases.
The majority of business users will access the data in this
information architecture from the data warehouse, using SQL-
based environments.
The Evolution of data warehousing :
8. Traditional Data Warehouse :
Complete record from transactional system.
All data centralized
Addition every month/day of new data
Analytics designed against stable environment
Many reports run on a production basis
10. Changing the Role of the Data Warehouse :
It is useful to think about the similarities and differences between the way
data is managed in the traditional data warehouse and when the warehouse
is combined with big data.
Similarities between the two data management methods
include :
Requirements for common data definitions
Requirements to extract and transform key data sources
The need to conform to required business processes and rules
11. Differences between the traditional data warehouse and big
data include :
The distributed computing model of big data will be
essential to allowing the hybrid model to be
operational.
The big data analysis will be the primary focus of the
efforts, while the traditional data warehouse will be
used to add historical and transactional business
context.
12. Big data stores will provide the capability to analyse
huge volumes of data in near real time.
A big data store will take the results of an analysis and
provide a mechanism to match the metadata of the
big data analysis to the requirements of the data
warehouse.
