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What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
What are the basic elements of data warehousing?
There are essentially four main components: Operational Source Systems, Data Staging Area, Data Presentation Area, and Data Access Tools. Although Operational Source Systems is part of the model, Kimball states "The source systems should really be thought of as outside the warehouse"Operational Source SystemsOperational Source Systems are those databases that hold all the transactional data. There is nearly no interaction with the Data Presentation Area and the Data Access Tools. The main priorities are processing, performance, and availability.Data Staging AreaThe Data Staging Area is temporary location where data from source systems is copied. A staging area is mainly required in a Data Warehousing Architecture for timing reasons. In short, all required data must be available before data can be integrated into the Data Warehouse.Due to varying business cycles, data processing cycles, hardware and network resource limitations and geographical factors, it is not feasible to extract all the data from all Operational databases at exactly the same time. (Source: data-warehouses.net)Data Presentation AreaThe data presentation area is considered to be a set of integrated data marts. A data mart is a subset of the data warehouse and represents select data regarding a specific business function (Inmon, 1999). An organization can have multiple data marts, each one relevant to the department for which it was designed. For example, the English department may have a data mart reflecting historical student data including demographics, placement scores, academic performance, and class schedules. The data contained in the data presentation area must be detailed and logically organized.Data Access ToolsData Access Tools are really all the software that can query the data in the data warehouse's presentation area. Data Access Tools are those like Cognos 10, SAP BeX, MicroStrategy, and Roambi. A data access tool can be as simple as an ad hoc query tool or as complex as a sophisticated data mining or modeling application.
What is the difference between data warehouse testing and traditional software testing?
ETL or Dataware Housing Testing:- ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database. Many data warehouses also incorporate data from non-OLTP systems such as text files, legacy systems and spreadsheets. Traditional (old) way of software testing: -Requirement -Design -Code & Build -Testing -Maintenance Unfortunately that is an erroneous methodology because the earlier you find an error - the more funds you can save. For example, fixing an error in maintenance is ten times more expensive than fixing it during execution. But many organizations have improved this way of thinking and choose modern way of software testing. In this philosophy testing should take place in every stage.
What is the purpose of factless fact table in Data Warehouse?
Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. They are mere key values with reference to the dimensions from which the stats can be collected. - Jayakumar
Can you walk on a path in a warehouse without safety shoes?
No, you will step on things that hurt
What is a data mart?
In the data warehouse, the access layer is a data mart. Data marts are subsets that are used for a specific department.
3 Distinguish between data warehouse and a data mart?
A database is a system for storing transactional data (OLTP). A data mart is a repository for analytical data (OLAP). A database is a collection of information about a single topic's various features and activities. Data from many subjects will be stored in a data mart. To learn more about data science please visit- Learnbay.co
How does Data Warehouse differ from Data Mart?
Data marts are combined into a data warehouse cannot be built alone without considering data marts. Both has equal importance to built proper data warehouse.
What is a smaller version of a data warehouse?
What are the three most common forms of data warehouses? is a smaller form of a data warehouse that is often used by a single department or function. An independent data mart is a tiny warehouse that is built for a strategic business unit (SBU) or a department, but it does not have a central data source (EDW). To learn more about data science please visit- Learnbay.co
What is data marts?
In the data warehouse, the access layer is a data mart. Data marts are subsets that are used for a specific department.
What is the purpose of data marts?
A Data Mart is a type of tool used in computing data. Data Warehouses, or data storage servers, use the Data Mart to ensure data is delivered to customers. It is also known as the "access layer" of the warehouse program.
Data mart usually can be constructed more rapidly and at lower cost than a data warehouse because?
Because it often concentrates on a single subject area or line of business, a data mart may be built more quickly and at a lower cost than a data warehouse.... displays data as it would be seen by end users. To learn more about data science please visit- Learnbay.co
What is data where housing and mining?
it's data warehouse....data warehouse: it is a collection of multiple databases or it it is repository of data.data mining it is the process of extracting data from data warehouse.
Which are the characteristics of data mart?
A data mart is a data repository that gathers data and similar items from certain users or workers. Its characteristics include being focused on one subject like finances or sales, only gathering data from a couple sources like the external data or central data warehouse, and being controlled by only one source.
Briefly describe three-tier Data warehouse architecture?
Three-Tier Architecture of Data WarehouseClient:-* GUI/Presentation logic* Query specification* Data Analysis* Report formatting* Data accessApplication/Data Mart Server:-* Summarizing* Filtering* Meta Data* Multidimensional view* Data accessData Warehouse Server:-* Data logic* Data services* Meta data* File services
What are the reasons for creating a data mart?
Data marts are created for various reasons. They're easy to access, easy to create, create collective views for groups, is not cluttered, and cost less to implement than a data warehouse.
What do you mean by data warehouse?
Data warehouse is a house where current as well as historical data can be stored.
