Data Warehousing - Interview Questions
Dear readers, these Data Warehousing Interview Questions have been designed especially to get you acquainted with the nature of questions you may encounter during your interview for the subject of Data Warehousing. As per my experience, good interviewers hardly planned to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer:
A: Data warehouse is Subject Oriented, Integrated, Time-Variant and Nonvolatile collection of data that support management's decision making process.
A: Subject oriented signifies that the data warehouse stores the information around a particular subject such as product, customer, sales etc.
A: Some applications include Financial services, Banking Services, Customer goods, Retail Sectors, Controlled Manufacturing.
A: OLAP is acronym of Online Analytical Processing and OLTP is acronym of Online Transactional Processing
A: Data warehouse contains the historical information that is made available for analysis of the business whereas the Operational database contains the current information that is required to run the business.
A: Data Warehouse can implement Star Schema, Snowflake Schema or the Fact Constellation Schema
A: Data Warehousing is the process of constructing and using the data warehouse.
A: Data Warehousing involves data cleaning, data integration and data consolidations.
A: The functions performed by Data warehouse tool and utilities are Data Extraction, Data Cleaning, Data Transformation, Data Loading and Refreshing
A: Data Extraction means gathering the data from multiple heterogeneous sources.
A: Metadata is simply defined as data about data. In other words we can say that metadata is the summarized data that lead us to the detailed data.
A: Metadata respiratory contains Definition of data warehouse, Business Metadata, Operational Metadata, Data for mapping from operational environment to data warehouse and the Alorithms for summarization
A: Data cube help us to represent the data in multiple dimensions. The data cube is defined by dimensions and facts.
A: The dimensions are the entities with respect to which an enterprise keep the records.
A: Data mart contains the subset of organisation-wide data. This subset of data is valuable to specific group of an organisation. in other words we can say that data mart contains only that data which is specific to a particular group.
A: The view over a operational data warehouse is known as virtual warehouse.
A: The stages are IT strategy, Education, Business Case Analysis, technical Blueprint, Build the version, History Load, Ad hoc query,Requirement Evolution, Automation, Extending Scope.
A: This Component performs the operations required to extract and load process. The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.
A: Extract the data from source system.Fast Load the extracted data into temporary data store.Perform simple transformations into structure similar to the one in the data warehouse.
A: Warehouse manager is responsible for the warehouse management process.The warehouse manager consist of third party system software, C programs and shell scripts.The size and complexity of warehouse manager varies between specific solutions.
A: The Warehouse Manager performs consistency and referential integrity checks, Creates the indexes, business views, partition views against the base data, transforms and merge the source data into the temporary store into the published data warehouse, Backup the data in the data warehouse and archives the data that has reached the end of its captured life.
A: Summary Information is the area in data warehouse where the predefined aggregations are kept.
A: Query Manager is responsible for directing the queries to the suitable tables.
A: There are four types of OLAP Server namely Relational OLAP, Multidimensional OLAP, Hybrid OLAP, Specialized SQL Servers
A: Multidimensional OLAP is faster than the Relational OLAP
A: The functions such as roll-up, drill-down, slice, dice, and pivot are performed by OLAP
A: Only one dimension is selected for the slice operation.
A: For dice operation two or more dimensions are selected for a given cube.
A: There is only one fact table in Star Schema.
A: The normalization split up the data into additional tables.
A: The snowflake schema uses the concept of normalization.
A: Normalization helps to reduce the data redundancy.
A: Data Mining Query Language (DMQL) id used for Schema Definition.
A: DMQL is based on Structured Query Language (SQL)
A: Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.
A: Data Marting involves Hardware & Software cost, Network access cost and Time cost.
What is Next?
Further, you can go through your past assignments you have done with the subject and make sure you are able to speak confidently on them. If you are fresher then interviewer does not expect you will answer very complex questions, rather you have to make your basics concepts very strong.
Second it really doesn't matter much if you could not answer few questions but it matters that whatever you answered, you must have answered with confidence. So just feel confident during your interview. We at tutorialspoint wish you best luck to have a good interviewer and all the very best for your future endeavor. Cheers :-)