Name itself implies that it is a self explanatory term. Cleaning of Orphan records, Data breaching business rules, Inconsistent data and missing information in a database.
32. What is Metadata?
Metadata is defined as data about the data. The metadata contains information like number of columns used, fix width and limited width, ordering of fields and data types of the fields.
33. What are loops in Datawarehousing?
In datawarehousing, loops are existing between the tables. If there is a loop between the tables, then the query generation will take more time and it creates ambiguity. It is advised to avoid loop between the tables.
34. Whether Dimension table can have numeric value?
Yes, dimension table can have numeric value as they are the descriptive elements of our business.
35. What is the definition of Cube in Dataware housing?
Cubes are logical representation of multidimensional data. The edge of the cube has the dimension members,and the body of the cube contains the data values.
36. What is called Dimensional Modelling?
Dimensional Modeling is a concept which can be used by dataware house designers to build their own dataware house. This model can be stored in two types of tables – Facts and Dimension table.
Fact table has facts and measurements of the business and dimension table contains the context of measurements.
37. What are the types of Dimensional Modeling?
There are three types of Dimensional Modeling and they are as follows:
38. What is surrogate key?
Surrogate key is nothing but a substitute for the natural primary key. It is set to be a unique identifier for each row that can be used for the primary key to a table.
39. What is the difference between ER Modeling and Dimensional Modeling?
ER modeling will have logical and physical model but Dimensional modeling will have only Physical model.
ER Modeling is used for normalizing the OLTP database design whereas Dimensional Modeling is used for de-normalizing the ROLAP and MOLAP design.
40. What are the steps to build the data warehouse?
Following are the steps to be followed to build the datawaerhouse:
Gathering business requirements
Identifying the necessary sources
Identifying the facts
Defining the dimensions
Defining the attributes
Redefine the dimensions and attributes if required
Organize the Attribute hierarchy
Assign unique Identifiers