What is Difference between ER Modeling and Dimensional Modeling?
ER modeling is used for normalizing the OLTP database design. Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design.
What is degenerate dimension table?
If a table contains the values, which are neither dimension nor measures is called degenerate dimensions.
Why is Data Modeling Important?
Data modeling is probably the most labor intensive and time consuming part of the development process. The goal of the data model is to make sure that the all data objects required by the database are completely and accurately represented. Because the data model uses easily understood notations and natural language , it can be reviewed and verified as correct by the end-users.
In computer science, data modeling is the process of creating a data model by applying a data model theory to create a data model instance. A data model theory is a formal data model description. When data modelling, we are structuring and organizing data. These data structures are then typically implemented in a database management system. In addition to defining and organizing the data, data modeling will impose (implicitly or explicitly) constraints or limitations on the data placed within the structure.
Managing large quantities of structured and unstructured data is a primary function of information systems. Data models describe structured data for storage in data management systems such as relational databases. They typically do not describe unstructured data, such as word processing documents, email messages, pictures, digital audio, and video. (Reference : Wikipedia)
What is surrogate key?
Surrogate key is a substitution for the natural primary key. It is just a unique identifier or number for each row that can be used for the primary key to the table. The only requirement for a surrogate primary key is that it is unique for each row in the table. It is useful because the natural primary key can change and this makes updates more difficult.Surrogated keys are always integer or numeric.
What is Data Mart?
A data mart (DM) is a specialized version of a data warehouse (DW). Like data warehouses, data marts contain a snapshot of operational data that helps business people to strategize based on analyses of past trends and experiences. The key difference is that the creation of a data mart is predicated on a specific, predefined need for a certain grouping and configuration of select data. A data mart configuration emphasizes easy access to relevant information (Reference : Wiki). Data Marts are designed to help manager make strategic decisions about their business.
What is the difference between OLAP and data warehouse?
Datawarehouse is the place where the data is stored for analyzing where as OLAP is the process of analyzing the data,managing aggregations, partitioning information into cubes for in depth visualization.
What is a Cube and Linked Cube with reference to data warehouse?
Cubes are logical representation of multidimensional data.The edge of the cube contains dimension members and the body of the cube contains data values. The linking in cube ensures that the data in the cubes remain consistent.
What is junk dimension?
A number of very small dimensions might be lumped together to form a single dimension, a junk dimension – the attributes are not closely related. Grouping of Random flags and text Attributes in a dimension and moving them to a separate sub dimension is known as junk dimension.
What is snapshot with reference to data warehouse?
You can disconnect the report from the catalog to which it is attached by saving the report with a snapshot of the data.
What is active data warehousing?
An active data warehouse provides information that enables decision-makers within an organization to manage customer relationships nimbly, efficiently and proactively.
What is the difference between data warehousing and business intelligence?
Data warehousing deals with all aspects of managing the development, implementation and operation of a data warehouse or data mart including meta data management, data acquisition, data cleansing, data transformation, storage management, data distribution, data archiving, operational reporting, analytical reporting, security management, backup/recovery planning, etc. Business intelligence, on the other hand, is a set of software tools that enable an organization to analyze measurable aspects of their business such as sales performance, profitability, operational efficiency, effectiveness of marketing campaigns, market penetration among certain customer groups, cost trends, anomalies and exceptions, etc. Typically, the term “business intelligence” is used to encompass OLAP, data visualization, data mining and query/reporting tools.
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