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Interview Qustionsmodify[1] | Data Warehouse | Object (Computer Science)

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INTERVIEW QUSTIONS: DATA WAREHOUSING QUESTIONS 1. Explain: Data warehouse Data warehousing is a relational database that is designed for querying, analyzing rather than transaction processing. It contains historical data that is derived from the transaction data; it’s included from other resources. The data warehouse environment includes an extraction, transformation and loading, on line analytical processing, client analysis tools and other application that manages and gathering data deliver to
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  INTERVIEW QUSTIONS: DATA WAREHOUSING QUESTIONS 1. Explain: Data warehouseData warehousing is a relational database that is designed for querying, analyzing rather than transaction processing. Itcontains historical data that is derived from the transaction data; it’s included from other resources. The data warehouseenvironment includes an extraction, transformation and loading, on line analytical processing, client analysis tools andother application that manages and gathering data deliver to business users.Characteristics of Data warehouse Subject Oriented Data warehouses are designed to help you analyze your data. For example, you might want to learn moreabout your company’s sales data. To do this, you could build a warehouse concentrating on sales. In thiswarehouse, you could answer questions like Who was our best customer for this item last year? This kind of focus on a topic, sales in this case, is what subject oriented means.  Integrated  Integration is closely related to subject orientation. Data warehouses need to have the data from disparate sources putinto a consistent format. This means that naming conflicts have to be resolved and problems like data being in differentunits of measure must be resolved. Nonvolatile Nonvolatile means that the data should not change once entered into the warehouse. This is logical because thepurpose of a warehouse is to analyze what has occurred.Time VariantMost business analysis requires analyzing trends. Because of this, analysts tend to need large amounts of data. This isvery much in contrast to OLTP systems, where performance requirements demand that historical data be moved to anarchive.2. Data warehousing Architecture?3. Define data mart?Data mart is a logical subset of data warehouse. It’s designed for focusing on the particular area. (It is an in depth of one particular subject area. Ex: Sales information. Production)It consists of two types: independent data mart, dependent data mart.4. Define independent data mart?We are taking the data from the OLTP system or operational systems that is called independent data mart.5. Define dependent data mart?We are taking the data from the OLAP system that is called dependent data mart.6. Data warehouse goals?  1 The data in the data warehouse are consistent. 2 The data warehouse provides corporate and organize data. 3 The data warehouse is the place where we publish used data. 4 The data warehouse is not just data, also a query and analysis data.7. Define SchemaSchema is a collection of relational database, such as tables, views and other objects.8. Star schemaStar schema is a simplest data warehouse schema and the diagram resembles Star. Star schema is represented as amulti dimensional data model. It consists of one fact table and one or more dimensional table.9. Define snowflake structure?Snowflake schema is a schema that any complicated dimension is split into store separate table. That is normalized or partially normalized. Slower than star approach. Need to go with this approach when we feel data is highly redundancyand creating problem to retrieve data.10. Define multi-star schema?More than one fact table connected with all the dimension table that is called multi star schema.11. What is Conformed Dimension? Why we are called Conformed dimension?Any one dimension table connected with all the fact table that is called conformed dimension.(Any one dimension table which is related to two facts will get relation with confirmed dimension. Means singledimension is going to share by two facts.)12. Fact tableFact table which it contains facts, aggregate facts and foreign keys to the (about all the) dimensions tables. Fact tablerepresents data usually additive and numeric.Examples Sales, Cost and Profits. And also which it’s satisfy the condition many to many relationship also fact tables.AdditiveDescribes a fact (or measure) that can be summarized through addition. An additive fact is the most common type of fact. Examples include Sales, Cost, and Profit. (Contrast with nonadditive, semi-additive.)Non-additiveDescribes a fact (or measure) that cannot be summarized through addition. An example includes Average. (Contrastwith additive, semi-additive.)Semi-additiveDescribes a fact (or measure) that can be summarized through addition along some, but not all, dimensions. Examplesinclude Headcount and On Hand Stock.(Contrast with additive, non additive)  13. Dimension Table Dimension table which is stored for the textual description about the dimension and it contains the primary key. 14. Define Lookup TableThe lookup table provides the detailed information about the attributes.15. Define AttributeAttributes represent a single type of information in a dimension. For example, year is an attribute in the Time dimension.16. Define factA fact is a collection of related data item consisting of measure and content data.16. Define ETLStands for Extraction, Transformation, and Loading. The movement of data from one area to another.  17. Difference between OLTP and OLAPOLTPOLAPTransaction processingQuery processinge-r modelingDimensional modelingNormalized formDemoralized formRelatively small databaseComparatively large databaseContains few indexes and many joinsContain many indexes & few joinsMany concurrent userFew concurrent usersStores all dataStores relevant dataOperator viewManager viewComment end time sensitiveHistorical orientedVolatile dataNon volatile data18. Define synonym  Alternative method to creating a view that include an entire table or view from another user is to create a synonym.18. Define composite keyAll the primary key is refer in the fact table.18. Meta Data?Meta data is a data definition about the data structures. It contains a detail description about the location, mapping andindex key.(Description about the source data.Contains information, which is controlled and maintained by ETL.)METADATA TYPES1.Source information.2.Target information. 3. Transformation.4.Mapping.5.Scheduling)19. Define fact less fact tables.It contains a fact but we can not measure it.1). Event tracking tables.2). Coverage tables20. Difference between data warehouse and data martDATAWAREHOUSEDATAMARTSCOPECORPORATELINE OF BUSINESS (LOB)SUBJECTSMULTIPLESINGLE SUBJECTDATASOURCESMANYFEWSIZE(TYPICAL)100GB-TB+<100GBIMPLEMENTATION TIMEMONTHS TO YEARSMONTH21. Define ODS
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