SQL SERVER – Data Warehousing Interview Questions and Answers – Part 2

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What are normalization forms?
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Describes the foreign key columns in fact table and dimension table?
Foreign keys of dimension tables are primary keys of entity tables.
Foreign keys of facts tables are primary keys of Dimension tables.

What is Data Mining?
Data Mining is the process of analyzing data from different perspectives and summarizing it into useful information.

What is the difference between view and materialized view?
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.

What is ER Diagram?
Entity Relationship Diagrams are a major data modelling tool and will help organize the data in your project into entities and define the relationships between the entities. This process has proved to enable the analyst to produce a good database structure so that the data can be stored and retrieved in a most efficient manner.
An entity-relationship (ER) diagram is a specialized graphic that illustrates the interrelationships between entities in a database. A type of diagram used in data modeling for relational data bases. These diagrams show the structure of each table and the links between tables.

What is ODS?
ODS is abbreviation of Operational Data Store. A database structure that is a repository for near real-time operational data rather than long term trend data. The ODS may further become the enterprise shared operational database, allowing operational systems that are being re-engineered to use the ODS as there operation databases.

What is ETL?
ETL is abbreviation of extract, transform, and load. ETL is software that enables businesses to consolidate their disparate data while moving it from place to place, and it doesn’t really matter that that data is in different forms or formats. The data can come from any source.ETL is powerful enough to handle such data disparities. First, the extract function reads data from a specified source database and extracts a desired subset of data. Next, the transform function works with the acquired data – using rules orlookup tables, or creating combinations with other data – to convert it to the desired state. Finally, the load function is used to write the resulting data to a target database.

What is VLDB?
VLDB is abbreviation of Very Large DataBase. A one terabyte database would normally be considered to be a VLDB. Typically, these are decision support systems or transaction processing applications serving large numbers of users.

Is OLTP database is design optimal for Data Warehouse?
No. OLTP database tables are normalized and it will add additional time to queries to return results. Additionally OLTP database is smaller and it does not contain longer period (many years) data, which needs to be analyzed. A OLTP system is basically ER model and not Dimensional Model. If a complex query is executed on a OLTP system, it may cause a heavy overhead on the OLTP server that will affect the normal business processes.

If de-normalized is improves data warehouse processes, why fact table is in normal form?
Foreign keys of facts tables are primary keys of Dimension tables. It is clear that fact table contains columns which are primary key to other table that itself make normal form table.

What are lookup tables?
A lookup table is the table placed on the target table based upon the primary key of the target, it just updates the table by allowing only modified (new or updated) records based on thelookup condition.

What are Aggregate tables?
Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions. It is always easy to retrieve data from aggregated tables than visiting original table which has million records. Aggregate tables reduces the load in the database server and increases the performance of the query and can retrieve the result quickly.

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.

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.

What is conformed fact?
Conformed dimensions are the dimensions which can be used across multiple Data Marts in combination with multiple facts tables accordingly.

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.

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.

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.

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

Complete Series of SQL Server Interview Questions and Answers
SQL SERVER – Data Warehousing Interview Questions and Answers – Introduction
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 1
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 2
SQL SERVER – Data Warehousing Interview Questions and Answers – Part 3
SQL SERVER – Data Warehousing Interview Questions and Answers Complete List Download

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SQL Server Interview Questions and Answers Complete List Download
SQL SERVER Database Coding Standards and Guidelines Complete List Download
Reference : Pinal Dave (https://blog.sqlauthority.com)