Datamart is something which consists of only one type of
data whereas datwarehouse consists of data of different
type.For example all the organisation data say data related
to finance department,HR,Banking dept are stored in
datawarehouse whereas in datamart say only finance data
will stored.So datawarehouse is a collection of different
A data mart is a subject oriented database which supports the business needs of individual departments within the enterprise.It is an subset of the enterprise data warehouse.It is also known as high performance query structures.
For a faster process create aggregate tables and write better sql so that the process would fast.
Data warehouse is made up of many datamarts. DWH contain many subject areas. However, data mart focuses on one subject area generally. E.g. If there will be DHW of bank then there can be one data mart for accounts, one for Loans etc. This is high-level definitions.
Version dimension is the SCD type II in real time it using because of it will maintain the current data and full historical data.
In scd type 2 we have flag, version and timestamp..Type 2 reveals about contain historical and current data.. Whenever the current data get's in version was change.. for eg: em id 1000 is staying in chennai it is v1, when his location changed to bangalore v2, again to delhi v3.. remember its only for em id 1000.
Data Mart is a segment of a data warehouse that can provide data for reporting and analysis on a section, unit, department or operation in the company, e.g. sales, payroll, production. Data marts are sometimes complete individual data warehouses which are usually smaller than the corporate data warehouse.
Metadata is data about data. E.g. if in data mart we are receiving any file. Then metadata will contain information like how many columns, file is fix width/limited, ordering of fields, data types of field etc.
Metadata is adata about data.meta data comes in picture when we need to knowabout how data is stored and where it is stored.metadata tool is helpfull in caputuring the business meta data and the following sections explain business metadata.metedata tools are using for gathering,storing,updating,and for retrieving the business and technical metadata of an org.
Drilling can be done in drill down, up, through, and across; scope is the overall view of the drill exercise.
Datamart is subset of Datawarehouse we can say a
datamart is collection of individual departmental
Where as datawarehouse in collection of datamart.
Data mart is a single subject and datawarehouse is a integration of multiple subjects
Data validation strategies are often heavily influenced by the architecture for the application. If the application is already in production it will be significantly harder to build the optimal architecture than if the application is still in a design stage. If a system takes a typical architectural approach of providing common services then one common component can filter all input and output thus optimizing the rules and minimizing efforts.
There are three main models to think about when designing a data validation strategy.
*Accept Only Known Valid Data
*Reject Known Bad Data
*Sanitize Bad Data
We cannot emphasize strongly enough that Accept Only Known Valid Data is the best strategy. We do however recognize that this isn't always feasible for political financial or technical reasons and so we describe the other strategies as well.
All three methods must check:
Data validation is generally done manually in DWH in this case if source and TGT are relational you need to create SQL scripts to validate source and target data and if source is Flat file or non relational database you can use excel if data is very less or create dummy tables to validate your ETL code.