Data Warehousing Interview Questions and answers [Part 2]

The General Sql Server Interview Questions consists the most frequently asked questions in Sql server. This list of 100+ questions guage your familiarity with the Sql Server platform. The q&a have been collected over a period of time from various blogs, forums and other similar Php sites

6.Data Warehousing Interview Question Part[2]

6.1 What is real time data-warehousing?

Data warehousing captures business activity data. Real-time data warehousing captures business activity data as it occurs. As soon as the business activity is complete and there is data about it, the completed activity data flows into the data warehouse and becomes available instantly. and analysis.

6.2 What are conformed dimensions?

Conformed dimensions mean the exact same thing with every possible fact table to which they are joined. They are common to the cubes.

6.3 What is conformed fact?

Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly.

6.4 How do you load the time dimension?

Time dimensions are usually loaded by a program that loops through all possible dates that may appear in the data. 100 years may be represented in a time dimension, with one row per day.

6.5 What is a level of Granularity of a fact table?

Level of granularity means level of detail that you put into the fact table in a data warehouse. Level of granularity would mean what detail are you willing to put for each transactional fact.

6.6 What are non-additive facts?

Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table. However they are not considered as useless. If there is changes in dimensions the same facts can be useful.

6.7 What is factless facts table?

A fact table which does not contain numeric fact columns it is called factless facts table.ss

6.8 What are slowly changing dimensions (SCD)?

SCD is abbreviation of Slowly changing dimensions. SCD applies to cases where the attribute for a record varies over time. There are three different types of SCD.
1) SCD1 : The new record replaces the original record. Only one
record exist in database - current data. 2) SCD2 : A new record is added into the customer dimension table. Two records exist in database - current data and previous history data.
3) SCD3 : The original data is modified to include new data. One record exist in database - new information are attached with old information in same row.

6.9 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 don't care.

6.10 What is BUS Schema?

BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts.

6.11What 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.

6.12 What Snow Flake Schema?

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.

6.13 Differences between star and snowflake schema?

Star schema - A single fact table with N number of Dimension, all dimensions will be linked directly with a fact table. This schema is denormalized and results in simple join and less complex query as well as faster results.
Snow schema - Any dimensions with extended dimensions are know 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 and very complex query as well as slower results.

6.14 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.

6.15 What is degenerate dimension table?

If a table contains the values, which are neither dimension nor measures is called degenerate dimensions.

6.16 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.

6.17 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.

6.18 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.

6.19 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.

6.20 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.