Please read the Introductory Post before continue reading interview question and answers.
A data warehouse is the main repository of an organization’s historical data, its corporate memory. It contains the raw material for management’s decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis, such as data mining, on the information without slowing down the operational systems (Ref: Wikipedia). Data warehousing collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time. It is a repository of integrated information, available for queries and analysis.
Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information and sometimes to the information itself. The purpose of BI is to support better business decision making. Thus, BI is also described as a decision support system (DSS).
BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data.
Dimensional table contains textual attributes of measurements stored in the facts tables. Dimensional table is a collection of hierarchies, categories and logic which can be used for user to traverse in hierarchical nodes.
Dimensional data model concept involves two types of tables and it is different from the 3rd normal form. This concept uses Facts table, which contains the measurements of the business, and Dimension table, which contains the context (dimension of calculation) of the measurements.
Fact table contains measurements of business process. Fact table contains the foreign keys for the dimension tables. For instance, if your business process is ’paper production’, ‘average production of paper by one machine’ or ‘weekly production of paper’ will be considered as the measurement of business process.
There are four different fundamental stages of Data Warehousing.
Data warehouses in this initial stage are developed by simply copying the database of an operational system to an off-line server where the processing load of reporting does not impact on the operational system’s performance.
Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems, and the data is stored in an integrated reporting-oriented data structure
Data warehouses at this stage are updated on a transaction or event basis, every time an operational system performs a transaction (e.g. an order or a delivery or a booking)
Data warehouses at this stage are used to generate activity or transactions that are passed back into the operational systems for use in the daily activity of the organization.
There are two different ways to load data in dimension tables.
All the constraints and keys are validated against the data before it is loaded; this way data integrity is maintained.
All the constraints and keys are disabled before the data is loaded. Once data is loaded, it is validated against all the constraints and keys. If data is found invalid or dirty, it is not included in index, and all future processes on this data are skipped.
Foreign keys of dimension tables are primary keys of entity tables.
Foreign keys of facts tables are primary keys of Dimension tables.
Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information.
A view takes the output of a query and makes it appear like a virtual table; and it can be used in place of tables.
A materialized view provides indirect access to table data by storing the results of a query in a separate schema object.
Reference: Pinal Dave (http://blog.SQLAuthority.com)