Types of Data warehouse:
Note: "Data Modeler" decide which model is best
There are three types of data warehouses:
1.Centralized Data Warehouse
2.Federated Data Warehouse
Centralized data warehouse: A centralized DWH is one in which data is stored in a single, large primary database. This database can be queried directly or used to feed data marts.
Federated data warehouse: For the purpose of simplifying the data we can go for Federated data warehouse
A federated DWH is an active union and cooperation across separate DWHs.
1. Different DWHs communicate
2. Requires active cooperation across multiple DWHs
3. No passive co-existence of separate systems
To build data warehouse there are two approaches:
Data Mart:Data Mart is nothing but subset of Data Warehouse and only single subject area.
Data mart is a decentralized subset of data found either in a data warehouse or as a standalone subset designed to support the unique business requirements of a specific decision-support system. A data mart is a subject-oriented database which supports the business needs of middle management like departments. A data mart is also called High Performance Query Structures (HPQS).
Dependent data marts: In the top-down approach data mart development depends on enterprise data warehouse. Such data marts are called as dependent data marts. Dependent data marts are marts that are fed directly by the DWH, sometimes supplemented with other feeds, such as external data Independent data marts: In the bottom -up approach data mart development is independent on enterprise data warehouse. Such data marts are called as independent data marts. Independent data marts are marts that are fed directly by external sources and do not use the DWH. Embedded data marts are marts that are stored within the central DWH. They can be stored relationally as files or cubes.
Data Mart Main Features:
1. Low cost
2. Contain less information than the warehouse
3. Easily understood and navigated than an enterprise data warehouse.
4. Within the range of divisional or departmental budgets
Data Mart Advantages:
1. Typically single subject area and fewer dimensions
2. Focused user needs
3. Limited scope
4. Optimum model for DWH construction
5. Very quick time to market (30-120 days)
1. Top-Down Approach
2. Bottom-Up Approach
Note: "Data Modeler" decide which model is best
There are three types of data warehouses:
1.Centralized Data Warehouse
2.Federated Data Warehouse
Centralized data warehouse: A centralized DWH is one in which data is stored in a single, large primary database. This database can be queried directly or used to feed data marts.
Federated data warehouse: For the purpose of simplifying the data we can go for Federated data warehouse
A federated DWH is an active union and cooperation across separate DWHs.
1. Different DWHs communicate
2. Requires active cooperation across multiple DWHs
3. No passive co-existence of separate systems
To build data warehouse there are two approaches:
Data Mart:Data Mart is nothing but subset of Data Warehouse and only single subject area.
Data mart is a decentralized subset of data found either in a data warehouse or as a standalone subset designed to support the unique business requirements of a specific decision-support system. A data mart is a subject-oriented database which supports the business needs of middle management like departments. A data mart is also called High Performance Query Structures (HPQS).
Dependent data marts: In the top-down approach data mart development depends on enterprise data warehouse. Such data marts are called as dependent data marts. Dependent data marts are marts that are fed directly by the DWH, sometimes supplemented with other feeds, such as external data Independent data marts: In the bottom -up approach data mart development is independent on enterprise data warehouse. Such data marts are called as independent data marts. Independent data marts are marts that are fed directly by external sources and do not use the DWH. Embedded data marts are marts that are stored within the central DWH. They can be stored relationally as files or cubes.
Data Mart Main Features:
1. Low cost
2. Contain less information than the warehouse
3. Easily understood and navigated than an enterprise data warehouse.
4. Within the range of divisional or departmental budgets
Data Mart Advantages:
1. Typically single subject area and fewer dimensions
2. Focused user needs
3. Limited scope
4. Optimum model for DWH construction
5. Very quick time to market (30-120 days)
1. Top-Down Approach
2. Bottom-Up Approach
Top-Down
Approach: This approach is developed by W.H.Inmon. According to him
first we need to develop enterprise data warehouse. Then from that enterprise
data warehouse develop subject orient databases.
Bottom-Up Approach:
This approach is developed by Ralph Kimball. According to him first we need to develop
the data marts to support the business needs of middle-level management. Then
integrate all the data marts into an enterprise data warehouse.
Top Down
|
Bottom Up
|
More planning and design initially
|
Can plan initially without waiting
for global infrastructure
|
Involve people from different
workgroups, departments
|
Built incrementally
|
Data marts may be built later from
Global DW
|
Can be built before or in parallel
with Global DW
|
Overall data model to be decided
upfront
|
Less complexity in design
|
High cost, lengthy process, time
consuming.
|
Low cost of Hardware and other
resources.
|
Involved people from different work
groups, departments.
|
It is built in the incremental
manner.
|
