`Hey,
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queries.
Below are major characteristics of data
warehouse:
- Subject-oriented –
A data warehouse is always a subject oriented as it delivers
information about a theme instead of organization’s current
operations. It can be achieved on specific theme. That means the
data warehousing process is proposed to handle with a specific
theme which is more defined. These themes can be sales,
distributions, marketing etc.
A data warehouse never put emphasis only current operations.
Instead, it focuses on demonstrating and analysis of data to make
various decision. It also delivers an easy and precise
demonstration around particular theme by eliminating data which is
not required to make the decisions.
- Integrated –
It is somewhere same as subject orientation which is made in a
reliable format. Integration means founding a shared entity to
scale the all similar data from the different databases. The data
also required to be resided into various data warehouse in shared
and generally granted manner.
A data warehouse is built by integrating data from various sources
of data such that a mainframe and a relational database. In
addition, it must have reliable naming conventions, format and
codes. Integration of data warehouse benefits in effective analysis
of data. Reliability in naming conventions, column scaling,
encoding structure etc. should be confirmed. Integration of data
warehouse handles various subject related warehouse.
- Time-Variant –
In this data is maintained via different intervals of time such as
weekly, monthly, or annually etc. It founds various time limit
which are structured between the large datasets and are held in
online transaction process (OLTP). The time limits for data
warehouse is wide-ranged than that of operational systems. The data
resided in data warehouse is predictable with a specific interval
of time and delivers information from the historical perspective.
It comprises elements of time explicitly or implicitly. Another
feature of time-variance is that once data is stored in the data
warehouse then it cannot be modified, alter, or updated.
A data mart is the access layer of a
data warehouse that is used to provide users with
data. Data marts are often seen
as small slices of the data warehouse.
Data warehouses typically house enterprise-wide
data, and information stored in a data
mart usually belongs to a specific department or team.
KEY DIFFERENCE
- Data Warehouse is a large repository of data collected from
different sources whereas Data Mart is only subtype of a data
warehouse.
- Data Warehouse is focused on all departments in an organization
whereas Data Mart focuses on a specific group.
- Data Warehouse designing process is complicated whereas the
Data Mart process is easy to design.
- Data Warehouse takes a long time for data handling whereas Data
Mart takes a short time for data handling.
- Data Warehouse implementation process takes 1 month to 1 year
whereas Data Mart takes a few months to complete the implementation
process.
Kindly revert for any queries
Thanks.