Please read the Introductory Post before continue reading interview question and answers.
What is Hybrid Slowly Changing Dimension?
Hybrid SCDs are combination of both SCD 1 and SCD 2. It may happen that in a table, some columns are important and we need to track changes for them, i.e. capture the historical data for them, whereas in some columns even if the data changes, we do not care.
What is BUS Schema?
BUS Schema consists of a master suite of confirmed dimension and standardized definition of facts.
What is a Star Schema?
Star schema is a type of organizing the tables such that we can retrieve the result from the database quickly in the warehouse environment.
What Snow Flake Schema?
In Snowflake Schema, each dimension has a primary dimension table, to which one or more additional dimensions can join. The primary dimension table is the only table that can join to the fact table.
Differences between the Star and Snowflake Schema?
Star schema: A single fact table with N number of dimensions; all dimensions will be linked directly with a fact table. This schema is de-normalized and results in simple join and less complex query as well as faster results.
Snow schema: Any dimension with extended dimensions is known as snowflake schema; dimensions maybe interlinked or may have one-to-many relationship with other tables. This schema is normalized, and results in complex join leading very complex query (as well as slower results).
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 values, which are neither dimension nor measures, then it is called a degenerate dimension table.
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. In data modeling, 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 a Surrogate Key?
A 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 should be 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 Junk Dimension?
A number of very small dimensions may get lumped together to form a single dimension, i.e. 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 a 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 the manager make strategic decisions about their business.
What is the Difference between OLAP and Data Warehouse?
Data Warehouse is the place where the data is stored for analysis, whereas 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 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 and backup/recovery planning. 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. Typically, the term ’business intelligence’ is used to encompass OLAP, data visualization, data mining and query/reporting tools. (Reference: Les Barbusinski)
What is MDS?
Master Data Services or MDS helps enterprises standardize the data people rely on to make critical business decisions. With Master Data Services, IT organizations can centrally manage critical data assets companywide and across diverse systems, enable more people to securely manage master data directly, and ensure the integrity of information over time. (Read more here)
Explain the Paradigm of Bill Inmon and Ralph Kimball.
Bill Inmon’s paradigm: Data warehouse is one part of the overall business intelligence system. An enterprise has one data warehouse, and data marts source their information from the data warehouse. In the data warehouse, information is stored in the 3rd normal form.
Ralph Kimball’s paradigm: Data warehouse is the conglomerate of all data marts within the enterprise. Information is always stored in the dimensional model.
Reference: Pinal Dave (http://blog.SQLAuthority.com)