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Operational Data Store
An operational data store is used for operational reporting and as a source of data for the Enterprise Data
Data
Warehouse (EDW). It is a complementary element to an EDW in a decision support landscape, and is used for operational reporting, controls and decision making, as opposed to the EDW, which is used for tactical and strategic decision support. An operational data store should not be confused with an enterprise data hub (EDH). An operational data store will take transactional data from one or more production system and loosely integrate it, in some respects it is still subject oriented, integrated and time variant, but without the volatility constraints
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Master Data
Master data represents the business objects which are agreed on and shared across the enterprise.[1] It can cover relatively static reference data, transactional, unstructured, analytical, hierarchical and meta data.[2] It is the primary focus of the Information Technology (IT) discipline of Master Data Management (MDM). While master data is often non-transactional in nature, it is not limited to non-transactional data, and often supports transactional processes and operations
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Federated Database System
A federated database system is a type of meta-database management system (DBMS), which transparently maps multiple autonomous database systems into a single federated database. The constituent databases are interconnected via a computer network and may be geographically decentralized. Since the constituent database systems remain autonomous, a federated database system is a contrastable alternative to the (sometimes daunting) task of merging several disparate databases. A federated database, or virtual database, is a composite of all constituent databases in a federated database system. There is no actual data integration in the constituent disparate databases as a result of data federation. Through data abstraction, federated database systems can provide a uniform user interface, enabling users and clients to store and retrieve data from multiple noncontiguous databases with a single query—even if the constituent databases are heterogeneous
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Special
Special
Special
or specials may refer to:Contents1 Music 2 Film and television 3 Other uses 4 See alsoMusic[edit] Special
Special
(album), a 1992
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Enterprise Architecture
Enterprise architecture (EA) is "a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a comprehensive approach at all times, for the successful development and execution of strategy. Enterprise architecture applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies
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Book
A book is a series of pages assembled for easy portability and reading, as well as the composition contained in it. The book's most common modern form is that of a codex volume consisting of rectangular paper pages bound on one side, with a heavier cover and spine, so that it can fan open for reading. Books have taken other forms, such as scrolls, leaves on a string, or strips tied together; and the pages have been of parchment, vellum, papyrus, bamboo slips, palm leaves, silk, wood, and other materials.[1] The contents of books are also called books, as are other compositions of that length. For instance, Aristotle's Physics, the constituent sections of the Bible, and even the Egyptian Book of the Dead
Book of the Dead
are called books independently of their physical form. Conversely, some long literary compositions are divided into books of varying sizes, which typically do not correspond to physically bound units
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Data Hub
A data hub is a collection of data from multiple sources organized for distribution, sharing, and often subsetting and sharing. Generally this data distribution is in the form of a hub and spoke architecture.Contents1 Features 2 List of products that promote themselves as data hubs 3 Approaches and considerations 4 See also 5 ReferencesFeatures[edit] A data hub differs from a data warehouse in that it is generally unintegrated and often at different grains. It differs from an operational data store because a data hub does not need to be limited to operational data. A data hub differs from a data lake by homogenizing data and possibly serving data in multiple desired formats, rather than simply storing it in one place, and by adding other value to the data such as de-duplication, quality, security, and a standardized set of query services
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Data Integration
Data
Data
integration involves combining data residing in different sources and providing users with a unified view of them.[1] This process becomes significant in a variety of situations[2], which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data
Data
integration appears with increasing frequency as the volume (that is, big data[3]) and the need to share existing data explodes.[4] It has become the focus of extensive theoretical work, and numerous open problems remain unsolved.Contents1 History 2 Example 3 Theory3.1 Definitions 3.2 Query processing4 In the life sciences 5 See also 6 ReferencesHistory[edit]Figure 1: Simple schematic for a data warehouse
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Data Virtualization
Data virtualization is any approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located,[1] and can provide a single customer view (or single view of any other entity) of the overall data.[2] Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data. This reduces the risk of data errors, of the workload moving data around that may never be used, and it does not attempt to impose a single data model on the data (an example of heterogeneous data is a federated database system). The technology also supports the writing of transaction data updates back to the source systems.[3] To resolve differences in source and consumer formats and semantics, various abstraction and transformation techniques are used
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International Standard Book Number
"ISBN" redirects here. For other uses, see ISBN (other).International Standard Book
Book
NumberA 13-digit ISBN, 978-3-16-148410-0, as represented by an EAN-13 bar codeAcronym ISBNIntroduced 1970; 48 years ago (1970)Managing organisation International ISBN AgencyNo. of digits 13 (formerly 10)Check digit Weighted sumExample 978-3-16-148410-0Website www.isbn-international.orgThe International Standard Book
Book
Number (ISBN) is a unique[a][b] numeric commercial book identifier. Publishers purchase ISBNs from an affiliate of the International ISBN Agency.[1] An ISBN is assigned to each edition and variation (except reprintings) of a book. For example, an e-book, a paperback and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, and 10 digits long if assigned before 2007
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Data Integrity
Data
Data
integrity is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle,[1] and is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context – even under the same general umbrella of computing. It is at times used as a proxy term for data quality,[2] while data validation is a pre-requisite for data integrity.[3] Data integrity is the opposite of data corruption.[4] The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities,) and upon later retrieval, ensure the data is the same as it was when it was originally recorded. In short, data integrity aims to prevent unintentional changes to information
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Business Rule
A business rule is a rule that defines or constrains some aspect of business and always resolves to either true or false. Business
Business
rules are intended to assert business structure or to control or influence the behavior of the business.[1] Business
Business
rules describe the operations, definitions and constraints that apply to an organization. Business
Business
rules can apply to people, processes, corporate behavior and computing systems in an organization, and are put in place to help the organization achieve its goals.[citation needed] For example, a business rule might state that no credit check is to be performed on return customers
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Data Cleaning
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.[1] Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores
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Data
Data
Data
(/ˈdeɪtə/ DAY-tə, /ˈdætə/ DAT-ə, /ˈdɑːtə/ DAH-tə)[1] is a set of values of qualitative or quantitative variables. Data
Data
and information are often used interchangeably; however, the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person
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Surrogate Key
A surrogate key (or synthetic key, entity identifier, system-generated key, database sequence number, factless key, technical key, or arbitrary unique identifier[citation needed]) in a database is a unique identifier for either an entity in the modeled world or an object in the database. The surrogate key is not derived from application data, unlike a natural (or business) key which is derived from application data.Contents1 Definition 2 Surrogates in practice 3 Advantages3.1 Immutability 3.2 Requirement changes 3.3 Performance 3.4 Compatibility 3.5 Uniformity 3.6 Validation4 Disadvantages4.1 Disassociation 4.2 Query optimization 4.3 Normalization 4.4 Business process modeling 4.5 Inadvertent disclosure 4.6 Inadvertent assumptions5 See also 6 ReferencesDefinition[edit] There are at least two definitions of a surrogate:Surrogate (1) – Hall, Owlett and Todd (1976) A surrogate represents an entity in the outside world
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Operational System
An operational system is a term used in data warehousing to refer to a system that is used to process the day-to-day transactions of an organization. These systems are designed in a manner that processing of day-to-day transactions is performed efficiently and the integrity of the transactional data is preserved. Synonyms[edit] Sometimes operational systems are referred to as operational databases, transaction processing systems, or online transaction processing systems (OLTP). However, the use of the last two terms as synonyms may be confusing, because operational systems can be batch processing systems as well. Any enterprise must necessarily maintain a lot of data about its operation.Organization ProbablyManufacturing Company Product dataBank Account DataHospital Patient DataUniversity Student DataGovernment Department Planning dataSee also[edit]Operating system (OS) Data warehouses versus operational systemsThis database-related article is a stub
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