These are the top Data Warehousing interview questions and answers that can help you crack your Data Warehousing job interview. You will learn about the difference between a Data Warehouse and a database, cluster analysis, chameleon method, Virtual Data Warehouse, snapshots, ODS for operational reporting, XMLA for accessing data, and types of slowly changing dimensions. Learn Data Warehousing from Intellipaat’s Data Warehousing Certification Training and excel in your career!
A database uses a relational model to store data, whereas a Data Warehouse uses various schemas such as star schema and others. In star schema, each dimension is represented by only the one-dimensional table. Data Warehouse supports dimensional modeling, which is a design technique to support end-user queries.
Cluster analysis is used to define the object without giving the class label. It analyzes all the data that is present in the Data Warehouse and compares the cluster with the cluster that is already running. It performs the task of assigning some set of objects into groups, also known as clusters. It is used to perform the data mining job using a technique like statistical data analysis. It includes all the information and knowledge around many fields such as Machine Learning, pattern recognition, image analysis, and bio-informatics. Cluster analysis performs the iterative process of knowledge discovery and includes trials and failures. It is used with the pre-processing and other parameters to achieve the properties that are desired to be used.
Purpose of cluster analysis:
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Chameleon is a hierarchical clustering algorithm that overcomes the limitations of the existing models and methods present in Data Warehousing. This method operates on the sparse graph having nodes that represent data items and edges which represent the weights of the data items.
This representation allows large datasets to be created and operated successfully. The method finds the clusters that are used in the dataset using the two-phase algorithm.
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A fact table is usually designed at a low level of granularity. This means that we need to find the lowest level of information that can be stored in a fact table e.g., employee performance is a very high level of granularity. Employee_performance_daily and employee_perfomance_weekly can be considered as lower levels of granularity.
The granularity is the lowest level of information stored in the fact table. The depth of the data level is known as granularity. In date dimension, the level could be year, month, quarter, period, week, and day of granularity.
The process consists of the following two steps:
The above factors of determination will be re-sent as per the requirements.
SCDs (slowly changing dimensions) are the dimensions in which the data changes slowly, rather than changing regularly on a time basis.
Three types of SCDs are used in Data Warehousing:
Multidimensional OLAP (MOLAP) is faster than Relational OLAP (ROLAP).
Hybrid SCDs are a combination of both SCD1 and SCD2.
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 have to bother. For such tables, we implement Hybrid SCDs, wherein some columns are Type 1 and some are Type 2.
As part of Struts Framework, we can develop the Action Servlets and the ActionForm Servlets and other servlet classes.
In case of ActionForm class, we can develop the validate() method. This method will return the ActionErrors object. In this method, we can write the validation code.
A very large database (VLDB) is a database that contains an extremely large number of tuples (database rows) or occupies an extremely large physical file system storage space. A one terabyte database would normally be considered to be a VLDB.
Time dimensions are usually loaded by a program that loops through all possible dates appearing in the data. It is not unusual for 100 years to be represented in a time dimension, with one row per day.
Both differ in the concept of building the Data Warehouse.
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good explanation of every concept.
Awesome collection…That what exactly I was waiting for..Good work..
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