“Data Warehouse Manager Frequently Asked Questions in various Warehouse Manager job interviews by interviewer. The set of questions are here to ensures that you offer a perfect answer posed to you. So get preparation for your new job interview”
Semi-additive measures are those where only a subset of aggregation function can be applied. Let’s say account balance. A sum() function on balance does not give a useful result but max() or min() balance might be useful. Consider price rate or currency rate. Sum is meaningless on rate; however, average function might be useful.
Data marts are generally designed for a single subject area. An organization may have data pertaining to different departments like Finance, HR, Marketing etc. stored in data warehouse and each department may have separate data marts. These data marts can be built on top of the data warehouse.
Following are the steps to be followed to build the datawaerhouse:
☛ Gathering business requirements
☛ Identifying the necessary sources
☛ Identifying the facts
☛ Defining the dimensions
☛ Defining the attributes
☛ Redefine the dimensions and attributes if required
☛ Organize the Attribute hierarchy
☛ Define Relationships
☛ Assign unique Identifiers
A Datamart is a specialized version of Datawarehousing and it contains a snapshot of operational data that helps the business people to decide with the analysis of past trends and experiences. A data mart helps to emphasizes on easy access to relevant information.
Yes, dimension table can have numeric value as they are the descriptive elements of our business.
BUS schema consists of suite of confirmed dimension and standardized definition if there is a fact tables.
A view is nothing but a virtual table which takes the output of the query and it can be used in place of tables.
A materialized view is nothing but an indirect access to the table data by storing the results of a query in a separate schema.
Name itself implies that it is a self explanatory term. Cleaning of Orphan records, Data breaching business rules, Inconsistent data and missing information in a database.
There are four stages of Datawarehousing:
Offline Operational Database
Offline Data Warehouse
Real Time Datawarehouse
Integrated Datawarehouse
An active datawarehouse is a datawarehouse that enables decision makers within a company or organization to manage customer relationships effectively and efficiently.
There are three types of Dimensional Modeling and they are as follows:
☛ Conceptual Modeling
☛ Logical Modeling
☛ Physical Modeling
Additive measures can be used with any aggregation function like Sum(), Avg() etc. Example is Sales Quantity etc.
For a long time in the past and also even today, Data warehouses are built to facilitate reporting on different key business processes of an organization, known as KPI. Today we often call this whole process of reporting data from data warehouses as "Data Analytics". Data warehouses also help to integrate data from different sources and show a single-point-of-truth values about the business measures (e.g. enabling Master Data Management).
A Partial backup in an operating system is a backup short of full backup and it can be done while the database is opened or shutdown.
A data Warehouse can implement star schema, snowflake schema, and fact constellation schema.
Data Marting involves hardware & software cost, network access cost, and time cost.
Only one dimension is selected for the slice operation.
The stages are:
☛ IT strategy
☛ Education
☛ Business Case Analysis
☛ technical Blueprint
☛ Build the version
☛ History Load
☛ Ad hoc query
☛ Requirement Evolution
☛ Automation
☛ Extending Scope
Agglomerative Hierarchical clustering method allows the clusters to be read from bottom to top and it follows this approach so that the program always reads from the sub-component first then moves to the parent whereas, divisive uses top-bottom approach in which the parent is visited first then the child.
Agglomerative hierarchical method consists of objects in which each object creates its own clusters and these clusters are grouped together to create a large cluster. It defines a process of merging that carries on till all the single clusters are merged together into a complete big cluster that will consists of all the objects of child clusters whereas, in divisive the parent cluster is divided into smaller cluster and it keeps on dividing till each cluster has a single object to represent.
The loading of the dimension table can be represented in two different ways:
Conventional (Slow): In this way, the constraints and keys goes for validations that are done against the data. These validations are performed before the loading of the data takes place in the data warehousing. This ensures the data integrity that is maintained throughout the loading phase. As the name suggests it is slower method that has to be implemented. This way deals in too much complexity in creating the database.
Direct (Fast): In this, all the constraints and the keys that are used with the data are disabled. The validations are performed after the data is loaded in data warehousing. These validations include everything that are applied on constraints and keys. Data are checked for invalid bit or dirty bit, if anything like this is found then the data is not included in the index and all the processes that require the data are skipped.
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