Q: Define Data Warehouse?
A: Data warehouse is Subject Oriented, Integrated, Time-Variant and Nonvolatile collection of data that support management's decision making process.
Q: What does the subject oriented data warehouse signifies?
A: Subject oriented signifies that the data warehouse stores the information around a particular subject such as product, customer, sales etc.
Q: List any five applications of Data Warehouse?
A: Some applications include Financial services, Banking Services, Customer goods, Retail Sectors, Controlled Manufacturing.
Q: What does OLAP and OLTP stand for?
A: OLAP is acronym of Online Analytical Processing and OLTP is acronym of Online Transactional Processing
Q: What is the very basic difference between data warehouse and Operational Databases?
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.
Q: List the Schema that Data Warehouse System implements ?
A: Data Warehouse can implement Star Schema, Snowflake Schema or the Fact Constellation Schema
Q: What is Data Warehousing?
A: Data Warehousing is the process of constructing and using the data warehouse.
Q: List the process that are involved in Data Warehousing?
A: Data Warehousing involves data cleaning, data integration and data consolidations.
Q: List the functions of data warehouse tools and utilities?
A: The functions performed by Data warehouse tool and utilities are Data Extraction, Data Cleaning, Data Transformation, Data Loading and Refreshing
Q: What do you mean by Data Extraction?
A: Data Extraction means gathering the data from multiple heterogeneous sources.
Q: Define Metadata?
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.
Q: What does MetaData Respiratory contains?
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
Q: How does a Data Cube help?
A: Data cube help us to represent the data in multiple dimensions. The data cube is defined by dimensions and facts.
Q: Define Dimension?
A: The dimensions are the entities with respect to which an enterprise keep the records.
Q: Explain Data mart?
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.
Q: What is Virtual Warehouse?
A: The view over a operational data warehouse is known as virtual warehouse.
Q: List the phases involved in Data warehouse delivery Process?
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.
Q: Explain Load Manager?
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.
Q: Define the function of Load Manager?
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.
Q: Explain Warehouse Manager?
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.
Q: Define functions of Warehouse Manager?
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.
Q: What is Summary Information?
A: Summary Information is the area in data warehouse where the predefined aggregations are kept.
Q: What does the Query Manager responsible for?
A: Query Manager is responsible for directing the queries to the suitable tables.
Q: List the types of OLAP server?
A: There are four types of OLAP Server namely Relational OLAP, Multidimensional OLAP, Hybrid OLAP, Specialized SQL Servers
Q: Which one is more faster Multidimensional OLAP or Relational OLAP?
A: Multidimensional OLAP is faster than the Relational OLAP
Q: List the functions performed by OLAP?
A: The functions such as roll-up, drill-down, slice, dice, and pivot are performed by OLAP
Q: How many dimensions are selected in Slice operation?
A: Only one dimension is selected for the slice operation.
Q: How many dimensions are selected in dice operation?
A: For dice operation two or more dimensions are selected for a given cube.
Q: How many fact tables are there in Star Schema?
A: There is only one fact table in Star Schema.
Q: What is Normalization?
A: The normalization split up the data into additional tables.
Q: Out of Star Schema and Snowflake Schema, the dimension table is normalised?
A: The snowflake schema uses the concept of normalization.
Q: What is the benefit of Normalization?
A: Normalization helps to reduce the data redundancy.
Q: Which language is used for defining Schema Definition
A: Data Mining Query Language (DMQL) id used for Schema Definition.
Q: What language is the base of DMQL
A: DMQL is based on Structured Query Language (SQL)
Q: What are the reasons for partitioning?
A: Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.
Q: What kind of costs are involved in Data Marting?
A: Data Marting involves Hardware & Software cost, Network access cost and Time cost.