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• Data Warehouse https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Conformed dimension 1 A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Conformed dimension 1 Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other. Most important, the row headers produced in two different answer sets from the same conformed dimension(s) must be able to match perfectly. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Conformed dimension Conformed dimensions are either identical or strict mathematical subsets of the most granular, detailed dimension 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Junk dimension 1 A junk dimension is a convenient grouping of typically low-cardinality flags and indicators https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Junk dimension 1 One solution is to create a new dimension for each of the remaining attributes, but due to their nature, it could be necessary to create a vast number of new dimensions resulting in a fact table with a very large number of foreign keys. The designer could also decide to leave the remaining attributes in the fact table but this could make the row length of the table unnecessarily large if, for example, the attributes is a long text string. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Junk dimension The solution to this challenge is to identify all the attributes and then put them into one or several Junk Dimensions 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Junk dimension Junk dimensions are also appropriate for placing attributes like non-generic comments from the fact table. Such attributes might consist of data from an optional comment field when a customer places an order and as a result will probably be blank in many cases. Therefore the junk dimension should contain a single row representing the blanks as a surrogate key that will be used in the fact table for every row returned with a blank comment field 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Degenerate dimension 1 A degenerate dimension is a key, such as a transaction number, invoice number, ticket number, or bill-of-lading number, that has no attributes and hence does not join to an actual dimension table. Degenerate dimensions are very common when the grain of a fact table represents a single transaction item or line item because the degenerate dimension represents the unique identifier of the parent. Degenerate dimensions often play an integral role in the fact table's primary key. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Role-playing dimension Dimensions are often recycled for multiple applications within the same database. For instance, a "Date" dimension can be used for "Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often referred to as a "role-playing dimension". 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Use of ISO representation terms 1 When referencing data from a metadata registry such as ISO/IEC 11179, representation terms such as Indicator (a boolean true/false value), Code (a set of non-overlapping enumerated values) are typically used as dimensions. For example using the National Information Exchange Model (NIEM) the data element name would be PersonGenderCode and the enumerated values would be male, female and unknown. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Common patterns 1 One of the reasons to have date dimensions is to place calendar knowledge in the data warehouse instead of hard coded in an application https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Common patterns Having both the date and time of day in the same dimension, may easily result in a huge dimension with millions of rows 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Common patterns As a rule of thumb, time of day dimension should only be created if hierarchical groupings are needed or if there are meaningful textual descriptions for periods of time within the day (ex. “evening rush” or “first shift”). 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Common patterns 1 If the rows in a fact table are coming from several timezones, it might be useful to store date and time in both local time and a standard time https://store.theartofservice.com/the-data-warehouse-toolkit.html Teradata - Active enterprise data warehouse Teradata Active Enterprise Data Warehouse is the platform that runs the Teradata Database, with added data management tools and data mining software. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Teradata - Active enterprise data warehouse The data warehouse differentiates between “hot and cold” data – meaning that the warehouse puts data that is not often used in a slower storage section. As of October 2010, Teradata uses Xeon 5600 processors for the server nodes. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Teradata - Active enterprise data warehouse 1 Teradata Database 13.10 was announced in 2010 as the company’s database software for storing and processing data. https://store.theartofservice.com/the-data-warehouse-toolkit.html Teradata - Active enterprise data warehouse Teradata Database 14 was sold as the upgrade to 13.10 in 2011 and runs multiple data warehouse workloads at the same time. It includes column-store analyses. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Teradata - Active enterprise data warehouse 1 Teradata Integrated Analytics is a set of tools for data analysis that resides inside the data warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance In computing, a data warehouse appliance is a marketing term for an integrated set of servers, storage, Operating System(s), DBMS and software specifically pre-installed and pre-optimized for data warehousing (DW). Alternatively, the term can also apply to similar software-only systems promoted as easy to install on specific recommended hardware configurations or preconfigured as a complete system. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance DW appliances are marketed to for middle-to-big data applications, most commonly on data volumes in the terabyte to petabyte range. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - Technology 1 Most DW appliances use massively parallel processing (MPP) architectures to provide high query performance and platform scalability https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - History 1 MPP database architectures have a long pedigree. Some consider Teradata's initial product as the first DW appliance — or Britton-Lee's. Teradata acquired Britton Lee — renamed ShareBase — in June, 1990. Others disagree, considering appliances as a "disruptive technology" for Teradata https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - History Open source and commodity computing components aided a reemergence of MPP data warehouse appliances 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - History Other DW appliance vendors use specialized hardware and advanced software, instead of MPP architectures. Netezza announced a "data appliance" in 2003, and used specialized fieldprogrammable gate array hardware. Kickfire followed in 2008 with what they called a dataflow "sql chip". 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - History In 2009 more DW appliances emerged 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouse appliance - History 1 The market has also seen the emergence of data-warehouse bundles where vendors combine their hardware and database software together as a data warehouse platform https://store.theartofservice.com/the-data-warehouse-toolkit.html Measure (data warehouse) In a data warehouse, a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Measure (data warehouse) - Example 1 For example if a retail store sold a specific product, the quantity and prices of each item sold could be added or averaged to find the total number of items sold and total or average price of the goods. https://store.theartofservice.com/the-data-warehouse-toolkit.html Measure (data warehouse) - Use of ISO representation terms 1 When entering data into a metadata registry such as ISO/IEC 11179, representation terms such as Number, Value and Measure are typically used as measures. https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) 1 ISBN 0-471-20024-7, Page 356 So the reason why aggregates can make such a dramatic increase in the performance of the data warehouse is the reduction of the number of rows to be accessed when responding to a query.Christopher Adamson, Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance, Wiley Publishing, Inc., 2006 ISBN 978-0-471-77709-0, Page 23 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) 1 Ralph Kimball|Kimball, who is widely regarded as one of the original architects of data warehousing, says: https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) The single most dramatic way to affect performance in a large data warehouse is to provide a proper set of aggregate (summary) records that coexist with the primary base records. Aggregates can have a very significant effect on performance, in some cases speeding queries by a factor of one hundred or even one thousand. No other means exist to harvest such spectacular gains. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) Having aggregates and atomic data increases the complexity of the dimensional model. This complexity should be transparent to the users of the data warehouse, thus when a request is made, the data warehouse should return data from the table with the correct grain. So when requests to the data warehouse are made, aggregate navigator functionality should be implemented, to 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) 1 The best way to choose this subset and decide which aggregations to build is to monitor queries and design aggregations to match query patterns.Ralph Kimball et al., The Data Warehouse Toolkit, Second Edition, Wiley Publishing, Inc., 2008 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Aggregate navigator The aggregate navigation essentially examines the query to see if it can be answered using a smaller, aggregate table.Ralph Kimball et al., The Data Warehouse Toolkit, Second Edition, Wiley Publishing, Inc., 2008 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Aggregate navigator 1 Implementations of aggregate navigators can be found in a range of technologies: https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Aggregate navigator *Business intelligence|BI application servers or query tools 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Aggregate navigator 1 It is generally recommended to use either of the first three technologies, since the benefits in the latter case is restricted to a single front end Business intelligence|BI toolRalph Kimball et al., The Data Warehouse Toolkit, Second Edition, Wiley Publishing, Inc., 2008. ISBN 978-0-47014977-5, Page 354 https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Problems/challenges 1 *Since dimensional models only gains from aggregates on large data sets, at what size of the data sets should one start considering using aggregates? https://store.theartofservice.com/the-data-warehouse-toolkit.html Aggregate (Data Warehouse) - Problems/challenges *Similarly, is a data warehouses always handling data sets that are too large for direct queries, or is it sometimes a good idea to omit the aggregate tables, when starting a new data warehouse project. Thus will, omitting aggregates in the first iteration of building a new data warehouse, make the structure of the dimensional model simpler? 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) In computing, a 'data warehouse' or 'enterprise data warehouse' ('DW', 'DWH', or 'EDW') is a database used for Business reporting|reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) The data stored in the warehouse are Uploading and downloading|uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) 1 The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) 1 A data mart is a small data warehouse focused on a specific area of interest. Data warehouses can be subdivided into data marts for improved performance and ease of use within that area. Alternatively, an organization can create one or more data marts as first steps towards a larger and more complex enterprise data warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) 1 This definition of the data warehouse focuses on data storage https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to : https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Congregate data from multiple sources into a single database so a single query engine can be used to present data. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Maintain data history, even if the source transaction systems do not. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Provide a single common data model for all data of interest regardless of the data's source. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Restructure the data so that it makes sense to the business users. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse 1 * Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Benefits of a data warehouse * Add value to operational business applications, notably customer relationship management (CRM) systems. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment The environment for data warehouses and marts includes the following: 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 * Source systems that provide data to the warehouse or mart; https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 *Data integration technology and processes that are needed to prepare the data for use; https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 *Different architectures for storing data in an organization's data warehouse or data marts; https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 *Different tools and applications for the variety of users; https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 *Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment In regards to source systems listed above, Rainer states, “A common source for the data in data warehouses is the company’s operational databases, which can be relational databases”. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 Regarding data integration, Rainer states, “It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse”. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment Rainer discusses storing data in an organization’s data warehouse or data marts. “There are a variety of possible architectures to store decisionsupport data”. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment Metadata are data about data. “IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures“. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Generic data warehouse environment 1 Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers. A “data warehouse” is a repository of historical data that are organized by subject to support decision makers in the organization. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the business data warehouse 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 Key developments in early years of data warehousing were: https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History * 1960s— General Mills and Dartmouth College, in a joint research project, develop the terms dimensions and facts.Kimball 2002, pg. 16 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History * 1970s— Bill Inmon begins to define and discuss the term: Data Warehouse 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History * 1975— Sperry Univac Introduce MAPPER (MAintain, Prepare, and Produce Executive Reports) is a database management and reporting system that includes the world's first 4GL. It was the first platform designed for building Information Centers (a forerunner of contemporary Enterprise Data Warehousing platforms) 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1983— Teradata introduces a database management system specifically designed for decision support. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1983— Sperry Corporation Martyn Richard Jones defines the Sperry Information Center approach, which while not being a true DW in the Inmon sense, did contain many of the characteristics of DW structures and process as defined previously by Inmon, and later by Devlin. First used at the Trustee Savings Bank|TSB England Wales https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1984— Metaphor Computer Systems, founded by David Liddle and Don Massaro, releases Data Interpretation System (DIS). DIS was a hardware/software package and GUI for business users to create a database management and analytic system. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1988— Barry Devlin and Paul Murphy publish the article [ http://ieeexplore.ieee.org/stamp/stamp.jsp ?tp=arnumber=5387658 An architecture for a business and information system] in IBM Systems Journal where they introduce the term business data warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1990— Red Brick Systems, founded by Ralph Kimball, introduces Red Brick Warehouse, a database management system specifically for data warehousing. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1991— Prism Solutions, founded by Bill Inmon, introduces Prism Warehouse Manager, software for developing a data warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 1992— Bill Inmon publishes the book Building the Data Warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History * 1995— The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History * 1996— Ralph Kimball publishes the book The Data Warehouse Toolkit. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - History 1 * 2000— Daniel Linstedt releases the Data Vault, enabling real time auditable Data Warehouses warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Facts 1 A fact is a value or measurement, which represents a fact about the managed entity or system. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Facts 1 E.g. if a BTS received 1,000 requests for traffic channel allocation, it allocates for 820 and rejects the remaining then it would report 3 'facts' or measurements to a management system: https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Facts 1 Facts at raw level are further aggregated to higher levels in various Dimension (data warehouse)|dimensions to extract more service or business-relevant information out of it. These are called aggregates or summaries or aggregated facts. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data There are three or more leading approaches to storing data in a data warehouse— the most important approaches are the dimensional approach and the normalized approach. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data The dimensional approach, whose supporters are referred to as “Kimballites”, believe in Ralph Kimball’s approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/star schema. The normalized approach, also called the 3NF model (Third Normal Form), whose supporters are referred to as “Inmonites”, believe in Bill Inmon's approach in which it is stated 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data In a Star schema|dimensional approach, transaction data are partitioned into facts, which are generally numeric transaction data, and dimension (data warehouse)|dimensions, which are the reference information that gives context to the facts 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 Also, the retrieval of data from the data warehouse tends to operate very quickly https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 # In order to maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated, and https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 # It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 The main advantage of this approach is that it is straightforward to add information into the database. A disadvantage of this approach is that, because of the number of tables involved, it can be difficult for users both to: https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 # join data from different sources into meaningful information and then https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 # access the information without a precise understanding of the sources of data and of the data structure of the data warehouse. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data It should be noted that both normalized and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the two models is the degree of normalization. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008). https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Dimensional vs. normalized approach for storage of data 1 In Information-Driven Business, Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 Ralph Kimball,Kimball 2002, pg. 310 designed an approach to data warehouse design known as bottomup. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 The data warehouse bus architecture is primarily an implementation of the bus, a collection of Dimension (data warehouse)#Types|conformed dimensions and Facts (data warehouse)#Types|conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 The integration of the data marts in the data warehouse is centered on the conformed dimensions (residing in the bus) that define the possible integration points between data marts https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 Business value can be returned as quickly as the first data marts can be created, and the method lends itself well to an exploratory and iterative approach to building data warehouses https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Bottom-up design 1 If integration via the bus is achieved, the data warehouse, through its two data marts, will not only be able to deliver the specific information that the individual data marts are designed to do, in this example either Sales or Production information, but can deliver integrated Sales-Production information, which, often, is of critical business value. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design Gartner released a research note confirming Inmon's definition in 2005Gartner, Of Data Warehouses, Operational Data Stores, Data Marts and Data Outhouses, Dec 2005 with additional clarity plus they added one additional attribute 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design 1 ; Subject-oriented: The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design ; Non-volatile: Data in the data warehouse are never over-written or deleted— once committed, the data are static, read-only, and retained for future reporting. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design ; Integrated: The data warehouse contains data from most or all of an organization's operational systems and these data are made consistent. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design 1 ; Time-variant: For an 'operational system', the stored data contains the current value. The data warehouse, however, contains the history of data values. https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design 1 ; No virtualization: A data warehouse is a physical repository.Gartner, Of Data Warehouses, Operational Data Stores, Data Marts and Data Outhouses, Dec 2005 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Top-down design 1 The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Hybrid design 1 Data warehouse (DW) solutions often resemble the hub and spokes architecture https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Hybrid design It is important to note that the DW database in a hybrid solution is kept on third normal form to eliminate data redundancy 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Hybrid design 1 The Data Vault model is geared to be strictly a data warehouse https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Data warehouses versus operational systems 1 Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Evolution in organization use 1 These terms refer to the level of sophistication of a data warehouse: https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Evolution in organization use 1 ; Offline operational data warehouse: Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented data https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Evolution in organization use ; Offline data warehouse: Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Evolution in organization use ; On time data warehouse: Online Integrated Data Warehousing represent the real time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Fact (data warehouse) - Evolution in organization use 1 ; Integrated data warehouse: These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems. https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouses In computing, a 'data warehouse' ('DW', 'DWH'), or an 'enterprise data warehouse' ('EDW'), is a database used for Business reporting|reporting (1) and data analysis (2). Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data warehouses store current as well as historical data and are used for 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouses 1 The data stored in the warehouse are Uploading and downloading|uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting. https://store.theartofservice.com/the-data-warehouse-toolkit.html Data warehouses - Benefits of a data warehouse 1 *Making decision–support queries easier to write. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) In a data warehouse, 'Dimensions' provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labeling. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) 1 A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) Typically dimensions in a data warehouse are organized internally into one or more hierarchies. Date is a common dimension, with several possible hierarchies: 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) *Days (are grouped into) Months (which are grouped into) Years, 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) 1 *Days (are grouped into) Weeks (which are grouped into) Years https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Conformed dimension 1 Dimensions are conformed when they are either exactly the same (including keys) or one is a perfect subset of the other. Most important, the row headers produced in two different answer sets from the same conformed dimension(s) must be able to match perfectly. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Conformed dimension 1 The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts.Ralph Kimball, Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Second Edition, Wiley Computer Publishing, 2002 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Junk dimension 1 A junk dimension is a convenient grouping of typically low-cardinality flags and indicators. By creating an abstract dimension, these flags and indicators are removed from the fact table while placing them into a useful dimensional framework.Ralph Kimball, Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Second Edition, Wiley Computer https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Degenerate dimension 1 Degenerate dimensions often play an integral role in the fact table's primary key.Ralph Kimball, Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, Second Edition, Wiley Computer Publishing, 2002 https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Role-playing dimension 1 Dimensions are often recycled for multiple applications within the same database. For instance, a Date dimension can be used for Date of Sale, as well as Date of Delivery, or Date of Hire. This is often referred to as a role-playing dimension. https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Use of ISO representation terms 1 When referencing data from a metadata registry such as ISO/IEC 11179, representation terms such as 'Indicator' (a boolean true/false value), 'Code' (a set of non-overlapping enumerated values) are typically used as dimensions. For example using the National Information Exchange Model (NIEM) the data element name would be 'PersonGenderCode' and the enumerated values would be 'male', https://store.theartofservice.com/the-data-warehouse-toolkit.html Dimension (data warehouse) - Common patterns 1 ;Date and timeRalph Kimball, The Data Warehouse Toolkit, Second Edition, Wiley Publishing, Inc., 2008. ISBN 978-0-47014977-5, Pages 253-256 https://store.theartofservice.com/the-data-warehouse-toolkit.html Measure (data warehouse) In a data warehouse, a 'measure' is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Operational database - Data warehouse terminology 1 In Data warehouse|data warehousing, the term is even more specific: the operational database is the one which is accessed by an operational system (for example a customer-facing website or the application used by the customer service department) to carry out regular operations of an organization. Operational databases usually use an online transaction processing database https://store.theartofservice.com/the-data-warehouse-toolkit.html Operational database - Data warehouse terminology The contents of the data warehouse are deleted from the operational database, but not necessarily updated in real time (if the machines are separate). Data warehouses tend to be optimized for faster read-only queries as an online analytical processing database to be used for back-office applications like business intelligence. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Real-time business intelligence - Data warehouse 1 An alternative approach to event driven architectures is to increase the refresh cycle of an existing data warehouse to update the data more frequently. These real-time data warehouse systems can achieve near real-time update of data, where the data latency typically is in the range from minutes to hours. The analysis of the data is still usually manual, so the total latency is significantly different from event driven architectural approaches. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 The generic structure, compared to the traditional data warehouse design based on third normal form schemas and Snowflake schema|snowflake or star schemas, has both advantages and disadvantages. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse * The generic structure can store Time variance|time variant business context data (i.e., changes to the business context data that happen over time such as a reorganization where departments are grouped differently), without requiring any database design changes 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * The generic structure presents a highly standardized approach to loading and retrieval, enabling the automatic creation of loading and retrieval routines by Kalido DIW. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse * The generic structure enables the loading of new classes of data through the simple addition of a few records of metadata. Conventionally, changes in requirements cause changes to the design, requiring a database administrator to alter the table structure of the warehouse and to reorganize the data in the database. The costs and time involved can be 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * The generic structure allows the capture of complex business rules that are difficult to capture using a conventional relational structure. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * The use of Meta data|metadata allows the structure of business context and transaction data to be easily understood by business users. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 A pure implementation of generic modeling principles will bring with it some disadvantages such as: https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * Conventional star schema can give better performance than physical implementations of the generic structure. Kalido DIW addresses these issues by combining elements of the generic structure with those of a star schema. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse * The generic structure supports the business structure by holding multiple rows, linked by pointers, instead of the conventional columns in a table. This makes the data difficult to read and the SQL difficult to write, requiring a codegenerating front-end to read and load data. Kalido DIW has such a codegenerating front-end. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse Despite the generic structure being different from conventional designs, it is far easier to query once understood as it combines the business metadata dictionary with the business context data. Finding out where something is stored is far simpler than navigating through hundreds of obscure tables. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse Given the above advantages and disadvantages, a mix of the generic design for business context data and the star schema for transaction data and retrieval would make an ideal situation. This has been the basis for the physical implementation of Kalido 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse DIW. The results of the Kalido implementation have proved that this innovative design can, and does, work. Kalido has UK patents on this design. The generic design of Kalido DIW is highly flexible but could have made processing transactions against the hierarchies of business context data it rather inefficient. To improve performance, the complex hierarchies 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse are automatically flattened out by Kalido DIW to create mapping tables. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 These mapping tables are complex and contain the full structure of the business context data hierarchies, including the date and time stamping of changes https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 The creation of mapping tables makes a Kalido warehouse appear like any other star schema. Conventional star schemas include the business context data, but they are keyed reference tables with all the attributes, classifications, etc. as columns. This causes duplication of data and difficulty in maintenance, but is fast to process. This is why the Kalido warehouse can equal the query https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 performance of a conventional design. The creation of the mapping tables can be a scheduled task or the user can initiate it. Batch tasks can also be used for business context data loading, transaction loading, summary generation, mapping table generation, https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse data mart building, or export of transaction or business context data. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 Data marts are generated by extracting information from the warehouse in a form that can be analyzed using tools such as Excel or https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 BusinessObjects to slice and dice, or drill-down through it. The data mart can be separated from the database, and small ones can take the form of Excel pivot tables, which can be taken away on a portable computer for offline analysis. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 In summary, one of the requirements of a data warehouse is that it should be capable of storing and managing almost any data from any source. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 In a Kalido warehouse: https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * Information is held in a neutral format, i.e. not limited to a particular type of business data. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * There are neutral formats for transaction data and business context data. https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 Metadata is used for: https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * validation and loading of data into the warehouse https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 * structuring data in the warehouse https://store.theartofservice.com/the-data-warehouse-toolkit.html Kalido - Generic Modeling and the Data Warehouse 1 The neutral formats allow you to select and view information as you want in data marts. https://store.theartofservice.com/the-data-warehouse-toolkit.html College football national championships in NCAA Division I FBS - College Football Data Warehouse recognized national champions These include the National Championship Foundation (1869–1882), the Helms Athletic Foundation (1883–1935), the College Football Researchers Association (1919–1935), the Associated Press Poll (1936–present), and the Coaches Poll (1950– present).[http://www.cfbdatawarehouse.com/da ta/national_championships/index.php College Football Data Warehouse: National Championships, accessdate=2009-01-30] From its research, it has compiled a list of Recognized National Championships for each season.[http://www.cfbdatawarehouse.com/data /national_championships/year_by_year.php College Football Data Warehouse: National Championships by Year, accessdate=2014-01-07] https://store.theartofservice.com/the-data-warehouse-toolkit.html Some years include recognition of multiple 1 Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) 1 The banking and financial markets industry is tackling three core challenges head on https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) IBM Banking and Financial Markets Data Warehouse typically support approximately 80% of business requirements and can be easily customized and extended to cover the specific requirements of a financial institution. They assist a financial institution in implementing a 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) 1 flexible, reusable, extensible and easily customizable architecture, which enables organizations to: https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) 1 # Increase adaptivity and faster response to changing customer needs https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) 1 # Accelerated Time to Value in the modeling, design and deployment phase of a project https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) # Proven design templates reduce project time and costs 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) # Strengthen Business/Technolog y Linkage 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) # Focus on achieving competitive differentiation 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking and Financial Markets Data Warehouse (BFMDW) 1 The BFMDW has a wealth of content, for example, the product contains more than 140 Analytical Requirements covering seven business focus areas. https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Banking Data Warehouse (BDW) 1 The BDW is a derivative of the BFMDW and contains content only relevant to the banking industry. https://store.theartofservice.com/the-data-warehouse-toolkit.html Information Framework - IBM Financial Markets Data Warehouse (FMDW) The FMDW is a derivative of the BFMDW and contains only content relevant to the Financial markets|financial markets industry. 1 https://store.theartofservice.com/the-data-warehouse-toolkit.html For More Information, Visit: • https://store.theartofservice.co m/the-data-warehousetoolkit.html The Art of Service https://store.theartofservice.com