Data Warehouse Developer Interview Preparation Guide
Optimize your Data Warehouse Developer interview preparation with our curated set of 55 questions. Our questions cover a wide range of topics in Data Warehouse Developer to ensure youre well-prepared. Whether youre new to the field or have years of experience, these questions are designed to help you succeed. Access the free PDF to get all 55 questions and give yourself the best chance of acing your Data Warehouse Developer interview. This resource is perfect for thorough preparation and confidence building.55 Data Warehouse Developer Questions and Answers:
1 :: Explain me why is chameleon method used in data warehousing?
Chameleon is a hierarchical clustering algorithm that overcomes the limitations of the existing models and the methods present in the data warehousing. This method operates on the sparse graph having nodes: that represent the data items, and edges: representing the weights of the data items.
This representation allows large dataset to be created and operated successfully. The method finds the clusters that are used in the dataset using two phase algorithm.
☛ The first phase consists of the graph partitioning that allows the clustering of the data items into large number of sub-clusters.
☛ Second phase uses an agglomerative hierarchical clustering algorithm to search for the clusters that are genuine and can be combined together with the sub-clusters that are produced.
This representation allows large dataset to be created and operated successfully. The method finds the clusters that are used in the dataset using two phase algorithm.
☛ The first phase consists of the graph partitioning that allows the clustering of the data items into large number of sub-clusters.
☛ Second phase uses an agglomerative hierarchical clustering algorithm to search for the clusters that are genuine and can be combined together with the sub-clusters that are produced.
2 :: Tell us what is Hybrid SCD?
Hybrid SCDs are a 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 have to bother.
For such tables, we implement Hybrid SCDs, where in some columns are Type 1 and some are Type 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 have to bother.
For such tables, we implement Hybrid SCDs, where in some columns are Type 1 and some are Type 2.
3 :: Tell us what is the main difference between Inmon and Kimball philosophies of data warehousing?
Both differ in the concept of building the data warehouse.
☛ Kimball views data warehousing as a constituency of Data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence, a unified view of the enterprise can be obtained from the dimension modeling on a local departmental level.
☛ Inmon explains in creating a data warehouse on a subject-by-subject area basis. Hence, the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.
☛ Hence, Kimball–First Data Marts–Combined way —Data warehouse
☛ Inmon—First Data warehouse–Later—-Data marts
☛ Kimball views data warehousing as a constituency of Data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence, a unified view of the enterprise can be obtained from the dimension modeling on a local departmental level.
☛ Inmon explains in creating a data warehouse on a subject-by-subject area basis. Hence, the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.
☛ Hence, Kimball–First Data Marts–Combined way —Data warehouse
☛ Inmon—First Data warehouse–Later—-Data marts
4 :: Can you list the Schema that a data warehouse system can implements?
A data Warehouse can implement star schema, snowflake schema, and fact constellation schema.
5 :: Tell us what is Virtual Warehouse?
The view over an operational data warehouse is known as virtual warehouse.
6 :: Tell us what does the Query Manager responsible for?
Query Manager is responsible for directing the queries to the suitable tables.
7 :: Tell us out of star schema and snowflake schema, whose dimension table is normalized?
Snowflake schema uses the concept of normalization.
8 :: Explain me what kind of costs are involved in Data Marting?
Data Marting involves hardware & software cost, network access cost, and time cost.
9 :: Explain me what is the purpose of cluster analysis in Data Warehousing?
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 compare the cluster with the cluster that is already running. It performs the task of assigning some set of objects into the groups also known as clusters. It is used to perform the data mining job using the technique like statistical data analysis. It includes all the information and knowledge around many fields like 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 as a result to achieve the properties that are desired to be used.
Purpose of cluster analysis :-
☛ Scalability
☛ Ability to deal with different kinds of attributes
☛ Discovery of clusters with attribute shape
☛ High dimensionality
☛ Ability to deal with noisy
☛ Interpretability
Purpose of cluster analysis :-
☛ Scalability
☛ Ability to deal with different kinds of attributes
☛ Discovery of clusters with attribute shape
☛ High dimensionality
☛ Ability to deal with noisy
☛ Interpretability
10 :: Tell us what is snapshot with reference to data warehouse?
☛ Snapshot refers to a complete visualization of data at the time of extraction. It occupies less space and can be used to back up and restore data quickly.
☛ A snapshot is a process of knowing about the activities performed. It is stored in a report format from a specific catalog. The report is generated soon after the catalog is disconnected.
☛ A snapshot is a process of knowing about the activities performed. It is stored in a report format from a specific catalog. The report is generated soon after the catalog is disconnected.