707:. These consist of metadata linking them to their parent hub or link, metadata describing the origin of the association and attributes, as well as a timeline with start and end dates for the attribute. Where the hubs and links provide the structure of the model, the satellites provide the "meat" of the model, the context for the business processes that are captured in hubs and links. These attributes are stored both with regards to the details of the matter as well as the timeline and can range from quite complex (all of the fields describing a client's complete profile) to quite simple (a satellite on a link with only a valid-indicator and a timeline).
319:. Both techniques have issues when dealing with changes in the systems feeding the data warehouse. For conformed dimensions you also have to cleanse data (to conform it) and this is undesirable in a number of cases since this inevitably will lose information. Data vault is designed to avoid or minimize the impact of those issues, by moving them to areas of the data warehouse that are outside the historical storage area (cleansing is done in the data marts) and by separating the structural items (business keys and the associations between the business keys) from the descriptive attributes.
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to make them unique. Let's say, "unique number". The latter key is not a real business key, so it is no hub. However, we do need to use it in order to guarantee the correct granularity for the link. In this case, we do not use a hub with surrogate key, but add the business key "unique number" itself to the link. This is done only when there is no possibility of ever using the business key for another link or as key for attributes in a satellite. This construct has been called a 'peg-legged link' by Dan
Linstedt on his (now defunct) forum.
969:). First you have to load all the hubs, creating surrogate IDs for any new business keys. Having done that, you can now resolve all business keys to surrogate ID's if you query the hub. The second step is to resolve the links between hubs and create surrogate IDs for any new associations. At the same time, you can also create all satellites that are attached to hubs, since you can resolve the key to a surrogate ID. Once you have created all the new links with their surrogate keys, you can add the satellites to all the links.
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and transport company. This could be a link called "Delivery". Referencing a link in another link is considered a bad practice, because it introduces dependencies between links that make parallel loading more difficult. Since a link to another link is the same as a new link with the hubs from the other link, in these cases creating the links without referencing other links is the preferred solution (see the section on loading practices for more information).
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whether this is the primary driver, the name of the insurance company for this car and person (could also be a separate hub) and a summary of the number of accidents involving this combination of vehicle and driver. Also included is a reference to a lookup- or reference table called R_RISK_CATEGORY containing the codes for the risk category in which this relationship is deemed to fall.
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of a historical database. If you use these keys as the backbone of a data warehouse, you can organize the rest of the data around them. This means that choosing the correct keys for the hubs is of prime importance for the stability of your model. The keys are stored in tables with a few constraints on the structure. These key-tables are called hubs.
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Links sometimes link hubs to information that is not by itself enough to construct a hub. This occurs when one of the business keys associated by the link is not a real business key. As an example, take an order form with "order number" as key, and order lines that are keyed with a semi-random number
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Links can link to other links, to deal with changes in granularity (for instance, adding a new key to a database table would change the grain of the database table). For instance, if you have an association between customer and address, you could add a reference to a link between the hubs for product
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The ETL is quite straightforward and lends itself to easy automation or templating. Problems occur only with links relating to other links, because resolving the business keys in the link only leads to another link that has to be resolved as well. Due to the equivalence of this situation with a link
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This is an example for a satellite on the drivers-link between the hubs for cars and persons, called "Driver insurance" (S_DRIVER_INSURANCE). This satellite contains attributes that are specific to the insurance of the relationship between the car and the person driving it, for instance an indicator
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Since the hubs are not joined to each other except through links, you can load all the hubs in parallel. Since links are not attached directly to each other, you can load all the links in parallel as well. Since satellites can be attached only to hubs and links, you can also load these in parallel.
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The business keys and their associations are structural attributes, forming the skeleton of the data model. The data vault method has as one of its main axioms that real business keys only change when the business changes and are therefore the most stable elements from which to derive the structure
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Data vault's philosophy is that all data is relevant data, even if it is not in line with established definitions and business rules. If data are not conforming to these definitions and rules then that is a problem for the business, not the data warehouse. The determination of data being "wrong" is
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For this purpose, the hubs and related satellites on those hubs can be considered as dimensions and the links and related satellites on those links can be viewed as fact tables in a dimensional model. This enables you to quickly prototype a dimensional model out of a data vault model using views.
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Teams using the data vault methodology should readily adapt to the repeatable, consistent, and measurable projects that are expected at CMMI Level 5. Data that flow through the EDW data vault system will begin to follow the TQM life-cycle that has long been missing from BI (business intelligence)
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Reference tables are referenced from
Satellites, but never bound with physical foreign keys. There is no prescribed structure for reference tables: use what works best in your specific case, ranging from simple lookup tables to small data vaults or even stars. They can be historical or have no
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Data vault modeling was originally conceived by Dan
Linstedt in the 1990s and was released in 2000 as a public domain modeling method. In a series of five articles in The Data Administration Newsletter the basic rules of the Data Vault method are expanded and explained. These contain a general
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Data vault attempts to solve the problem of dealing with change in the environment by separating the business keys (that do not mutate as often, because they uniquely identify a business entity) and the associations between those business keys, from the descriptive attributes of those keys.
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According to Dan
Linstedt, the Data Model is inspired by (or patterned off) a simplistic view of neurons, dendrites, and synapses – where neurons are associated with Hubs and Hub Satellites, Links are dendrites (vectors of information), and other Links are synapses (vectors in the opposite
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Links contain the surrogate keys for the hubs that are linked, their own surrogate key for the link and metadata describing the origin of the association. The descriptive attributes for the information on the association (such as the time, price or amount) are stored in structures called
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an interpretation of the data that stems from a particular point of view that may not be valid for everyone, or at every point in time. Therefore the data vault must capture all data and only when reporting or extracting data from the data vault is the data being interpreted.
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in Data Vault 2.0) and a presentation layer (data mart), and handling of data quality services and master data services), and the model. Within the methodology, the implementation of best practices is defined. Data Vault 2.0 has a focus on including new components such as
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Usually the attributes are grouped in satellites by source system. However, descriptive attributes such as size, cost, speed, amount or color can change at different rates, so you can also split these attributes up in different satellites based on their rate of change.
326:"The Data Vault Model is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and
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Note that while it is relatively straightforward to move data from a data vault model to a (cleansed) dimensional model, the reverse is not as easy, given the denormalized nature of the dimensional model's fact tables, fundamentally different to the
382:- and also focuses on the performance of the existing model. The old specification (documented here for the most part) is highly focused on data vault modeling. It is documented in the book: Building a Scalable Data Warehouse with Data Vault 2.0.
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This is an example of a reference table with risk categories for drivers of vehicles. It can be referenced from any satellite in the data vault. For now we reference it from satellite S_DRIVER_INSURANCE. The reference table is R_RISK_CATEGORY.
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requirements in the USA and similar measures in Europe this is a relevant topic for many business intelligence implementations, hence the focus of any data vault implementation is complete traceability and auditability of all information.
437:. The data vault model actually provides a "graph based" model with hubs and relationships in a relational database world. In this manner, the developer can use SQL to get at graph-based relationships with sub-second responses.
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ratings. They can be created and dropped on the fly in accordance with learning about relationships that currently don't exist. The model can be automatically morphed, adapted, and adjusted as it is used and fed new structures.
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Reference tables are a normal part of a healthy data vault model. They are there to prevent redundant storage of simple reference data that is referenced a lot. More formally, Dan
Linstedt defines reference data as follows:
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Associations or transactions between business keys (relating for instance the hubs for customer and product with each other through the purchase transaction) are modeled using link tables. These tables are basically
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Dirk Lerner: Data Vault für agile Data-Warehouse-Architekturen. In: Stephan
Trahasch, Michael Zimmer (Hrsg.): Agile Business Intelligence. Theorie und Praxis. dpunkt.verlag, Heidelberg 2016, ISBN 978-3-86490-312-0,
466:. The descriptive attributes for the information on the Hub (such as the description for the key, possibly in multiple languages) are stored in structures called Satellite tables which will be discussed below.
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All the tables contain metadata, minimally describing at least the source system and the date on which this entry became valid, giving a complete historical view of the data as it enters the data warehouse.
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It is necessary to evolve the specification to include the new components, along with the best practices in order to keep the EDW and BI systems current with the needs and desires of today's businesses.
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has arrived on the scene as of 2013 and brings to the table Big Data, NoSQL, unstructured, semi-structured seamless integration, along with methodology, architecture, and implementation best practices.
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history, but it is recommended that you stick to the natural keys and not create surrogate keys in that case. Normally, data vaults have a lot of reference tables, just like any other Data
Warehouse.
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of the
Enterprise in the sense that it describes the terms in the domain of the enterprise (Hubs) and the relationships among them (Links), adding descriptive attributes (Satellites) where necessary.
179:" where data that does not conform to the definitions is removed or "cleansed". A data vault enterprise data warehouse provides both; a single version of facts and a single source of truth.
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Data vault modeling makes no distinction between good and bad data ("bad" meaning not conforming to business rules). This is summarized in the statement that a data vault stores "
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Any information deemed necessary to resolve descriptions from codes, or to translate keys in to (sic) a consistent manner. Many of these fields are "descriptive" in nature and
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Another issue to which data vault is a response is that more and more there is a need for complete auditability and traceability of all the data in the data warehouse. Due to
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Dani
Schnider, Claus Jordan u. a.: Data Warehouse Blueprints. Business Intelligence in der Praxis. Hanser, München 2016, ISBN 978-3-446-45075-2, S. 35–37, 161–173.
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Daniel
Linstedt: Super Charge Your Data Warehouse. Invaluable Data Modeling Rules to Implement Your Data Vault. Linstedt, Saint Albans, Vermont 2011, ISBN 978-1-4637-7868-2.
879:(*) at least one attribute is mandatory. (**) sequence number becomes mandatory if it is needed to enforce uniqueness for multiple valid satellites on the same hub or link.
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in a data vault must be accompanied by record source and load date attributes, enabling an auditor to trace values back to the source. The concept was published in 2000 by
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John Giles: The Elephant in the Fridge. Guided Steps to Data Vault Success through Building Business-Centered Models. Technics, Basking Ridge 2019, ISBN 978-1-63462-489-3.
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coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed and
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The data vault modelled layer is normally used to store data. It is not optimised for query performance, nor is it easy to query by the well-known query-tools such as
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The hubs and links form the structure of the model, but have no temporal attributes and hold no descriptive attributes. These are stored in separate tables called
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Daniel Linstedt, Michael Olschimke: Building a Scalable Data Warehouse with Data Vault 2.0. Morgan Kaufmann, Waltham, Massachusetts 2016, ISBN 978-0-12-802510-9.
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A hub is not allowed to contain multiple business keys, except when two systems deliver the same business key but with collisions that have different meanings.
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for build out and deployment. Data vault projects have a short, scope-controlled release cycle and should consist of a production release every 2 to 3 weeks.
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are well-suited for capturing changes that occur when a source system is changed or added, but are considered advanced techniques which require experienced
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The modeling method is designed to be resilient to change in the business environment where the data being stored is coming from, by explicitly separating
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An effectivity satellite is a satellite built on a link, "and record the time period when the corresponding link records start and end effectivity".
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a specific state of the other more important information. As such, reference data lives in separate tables from the raw Data Vault tables
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This can happen if, for instance, you have a hub COURSE and the name of the course is an attribute but in several different languages.
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This is an example for a link-table between two hubs for cars (H_CAR) and persons (H_PERSON). The link is called "Driver" (L_DRIVER).
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modeling there are two well-known competing options for modeling the layer where the data are stored. Either you model according to
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overview, an overview of the components, a discussion about end dates and joins, link tables, and an article on loading practices.
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Patrick Cuba: The Data Vault Guru. A Pragmatic Guide on Building a Data Vault. Selbstverlag, ohne Ort 2020, ISBN 979-86-9130808-6.
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Kent Graziano: Better Data Modeling. An Introduction to Agile Data Engineering Using Data Vault 2.0. Data Warrior, Houston 2015.
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Hans Hultgren: Modeling the Agile Data Warehouse with Data Vault. Brighton Hamilton, Denver u. a. 2012, ISBN 978-0-615-72308-2.
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to multiple hubs, this difficulty can be avoided by remodeling such cases and this is in fact the recommended practice.
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optionally, you can also have metadata fields with information about manual updates (user/time) and the extraction date.
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Level 5 best practices. It includes multiple components of CMMI Level 5, and combines them with best practices from
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loading as much as possible, so that very large implementations can scale out without the need for major redesign.
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An alternative (and seldom used) name for the method is "Common Foundational Integration Modelling Architecture."
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Cheat sheet reflecting the rules in v1.0.8 and additional clarification from the forums on the rules in v1.0.8.
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as "all the data, all of the time") as opposed to the practice in other data warehouse methods of storing "a
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Load End Date (enddate) for the validity of this combination of attribute values for parent key L_DRIVER_ID
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Load Date (startdate) for the validity of this combination of attribute values for parent key L_DRIVER_ID
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An ID into a table with audit information, such as load time, duration of load, number of lines, etc.
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An ID into a table with audit information, such as load time, duration of load, number of lines, etc.
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An ID into a table with audit information, such as load time, duration of load, number of lines, etc.
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In its early days, Dan Linstedt referred to the modeling technique which was to become data vault as
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Data is never deleted from the data vault, unless you have a technical error while loading data.
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Hubs contain a list of unique business keys with low propensity to change. Hubs also contain a
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The business key that drives this hub. Can be more than one field for a composite business key
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et al. Since these end-user computing tools expect or prefer their data to be contained in a
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This is an example for a hub-table containing cars, called "Car" (H_CAR). The driving key is
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330:. The design is flexible, scalable, consistent and adaptable to the needs of the enterprise"
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Simple data vault model with two hubs (blue), one link (green) and four satellites (yellow)
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the record source, which can be used to see what system loaded each business key first.
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Dan Linstedt, the creator of the method, describes the resulting database as follows:
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direction). By using a data mining set of algorithms, links can be scored with
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Verhagen, K.; Vrijkorte, B. (June 10, 2008). "Relationeel versus Data Vault".
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369:, etc..), the architecture (amongst others an input layer (data stage, called
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Ketelaars, M.W.A.M. (2005-11-25). "Datawarehouse-modelleren met Data Vault".
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modeling method that is designed to provide long-term historical storage of
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The risk category for the driver. This is a reference to R_RISK_CATEGORY
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The recordsource of the information in this satellite when first loaded
1105: – System or repository of data stored in its natural/raw format
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263:. Statements consisting only of original research should be removed.
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The name of the insurance company for this vehicle and this driver
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The homepage of Dan Linstedt, the inventor of Data Vault modeling
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353:. The new specification consists of three pillars: methodology (
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1044:(TQM), and SDLC. Particularly, it is focused on Scott Ambler's
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for updating a data vault model is fairly straightforward (see
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Indicator whether the driver is the primary driver for this car
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surrogate key for the person hub, the second anchor of the link
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Building a scalable datawarehouse with data vault 2.0, p. xv
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Building a scalable datawarehouse with data vault 2.0, p. 11
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for each Hub item and metadata describing the origin of the
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Building a scalable datawarehouse with data vault 2.0, p. 6
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Sequence ID and surrogate key for the satellite on the link
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surrogate key for the car hub, the first anchor of the link
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models, but anchor models have a more normalized approach.
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The number of accidents by this driver in this vehicle
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The recordsource of this association when first loaded
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Thomas C. Hammergren; Alan R. Simon (February 2009).
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Oracle Business Intelligence Suite Enterprise Edition
477:, used to connect the other structures to this table.
1134: – Location where items are gathered before use
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Pages displaying wikidata descriptions as a fallback
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Pages displaying wikidata descriptions as a fallback
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Another view is that a data vault model provides an
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433:Another way to think of a data vault model is as a
49:. Unsourced material may be challenged and removed.
497:Hubs should normally have at least one satellite.
1609:"Data Vault Series 3 – End Dates and Basic Joins"
1549:Ronald Damhof; Lidwine van As (August 25, 2008).
1220:"Rålager istället för ett strukturerat datalager"
469:The Hub contains at least the following fields:
1333:Data Vault Series 3 – End Dates and Basic Joins
557:The record source of this key when first loaded
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2356:Data warehousing products and their producers
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1588:"Data Vault Series 2 – Data Vault Components"
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148:to change as well as emphasizing the need to
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1567:"Data Vault Series 1 – Data Vault Overview"
1322:Data Vault Series 2 – Data Vault Components
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1697:"Data Vault Modeling Specification v1.0.9"
1418:, page 61, why are business keys important
634:Sequence ID and surrogate key for the Link
293:common foundational warehouse architecture
1651:"Data Vault Series 5 – Loading Practices"
1532:Data Warehousing for Dummies, 2nd edition
1311:Data Vault Series 1 – Data Vault Overview
953:(*) at least one attribute is mandatory.
532:Sequence ID and surrogate key for the hub
297:common foundational modeling architecture
279:Learn how and when to remove this message
217:. Both data vaults and anchor models are
109:Learn how and when to remove this message
1448:Data Vault Modeling Specification v1.0.9
1111: – Centralized storage of knowledge
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1722:"Data Vault Loading Specification v1.2"
1359:Data Vault Series 5 – Loading Practices
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1028:The data vault methodology is based on
967:Data Vault Series 5 – Loading Practices
1672:"Data Vault Rules v1.0.8 Cheat Sheet"
1008:, a conversion is usually necessary.
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1803:(7). Array Publications B.V.: 36–40.
1657:. The Data Administration Newsletter
1636:. The Data Administration Newsletter
1615:. The Data Administration Newsletter
1594:. The Data Administration Newsletter
1573:. The Data Administration Newsletter
984:Data vault and dimensional modelling
47:adding citations to reliable sources
1630:"Data Vault Series 4 – Link Tables"
1431:Data Vault Forum, Standards section
1099: – American computer scientist
349:is the new specification. It is an
307:, with conformed dimensions and an
190:. Data vault is designed to enable
152:where all the data in the database
2241:MultiDimensional eXpressions (MDX)
1812:(4). Array Publications B.V.: 6–9.
944:A description of the risk category
14:
1746:"A short intro to #datavault 2.0"
1459:Effectivity Satellites - dbtvault
1344:Data Vault Series 4 – Link tables
1246:"Datamodeller för data warehouse"
1770:"Data Vault 2.0 Being Announced"
1513:Super Charge your Data Warehouse
1482:Super Charge your Data Warehouse
1470:Super Charge your Data Warehouse
1416:Super Charge your data warehouse
1404:Super Charge your Data Warehouse
1206:Super Charge your data warehouse
1194:Super Charge your data warehouse
1162:Super Charge your data warehouse
233:
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1511:Linstedt, Dan (December 2010).
1382:A short intro to #datavault 2.0
34:needs additional citations for
2262:Business intelligence software
2141:Extract, load, transform (ELT)
2136:Extract, transform, load (ETL)
1776:. Dan Linstedt. Archived from
1752:. Dan Linstedt. Archived from
1728:. Dan Linstedt. Archived from
1703:. Dan Linstedt. Archived from
1393:Data Vault 2.0 Being Announced
1300:A short intro to#datavault 2.0
933:The code for the risk category
1:
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169:a single version of the facts
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1370:Data Warehousing for Dummies
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1060:Some examples of tools are:
587:tables, with some metadata.
311:, or you model according to
1997:Ensemble modeling patterns
1967:Single version of the truth
1288:The New Business Supermodel
1138:Agile Business Intelligence
603:which are discussed below.
409:Alternative interpretations
259:the claims made and adding
177:single version of the truth
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2351:Comparison of OLAP servers
1406:, paragraph 5.20, page 110
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1905:Creating a data warehouse
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1560:. Array Publications B.V.
1534:. John Wiley & Sons.
1484:, paragraph 8.0, page 149
1472:, paragraph 8.0, page 146
756:Recommended but optional
640:Recommended but optional
538:Recommended but optional
1810:Database Magazine (DB/M)
1801:Database Magazine (DB/M)
1558:Database Magazine (DB/M)
1042:total quality management
156:. This means that every
16:Database modeling method
2246:XML for Analysis (XMLA)
1433:, section 3.0 Hub Rules
1174:The next generation EDW
371:persistent staging area
2178:Using a data warehouse
2033:Operational data store
1793:Dutch language sources
1065:2150 Datavault Builder
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225:History and philosophy
209:(3NF), data vault and
184:structural information
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2195:Business intelligence
1115:The Kimball lifecycle
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747:S_DRIVER_INSURANCE_ID
203:dimensional modelling
171:" (also expressed by
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58:"Data vault modeling"
2011:Focal point modeling
1983:Column-oriented DBMS
1932:Dimensional modeling
998:SAP Business Objects
205:) and the classical
43:improve this article
2316:Information factory
2089:Early-arriving fact
2006:Data vault modeling
1957:Reverse star schema
1707:on 30 November 2012
1670:Kunenborg, Ronald.
1290:, glossary, page 75
1020:of the data vault.
309:enterprise data bus
134:data vault modeling
2267:Reporting software
1681:. Grundsätzlich IT
941:RISK_CATEGORY_DESC
930:R_RISK_CATEGORY_CD
845:R_RISK_CATEGORY_CD
812:IND_PRIMARY_DRIVER
315:with the database
244:possibly contains
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2063:Sixth normal form
1655:Data Vault Series
1634:Data Vault Series
1613:Data Vault Series
1592:Data Vault Series
1571:Data Vault Series
1541:978-0-470-40747-9
1522:978-0-9866757-1-3
1070:Astera DW Builder
1046:agile methodology
1018:third normal form
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301:data warehouse
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441:Basic notions
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305:Ralph Kimball
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99:November 2016
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60: –
59:
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54:Find sources:
48:
44:
38:
37:
32:This article
30:
26:
21:
20:
2335:Dan Linstedt
2005:
1809:
1800:
1782:. Retrieved
1778:the original
1773:
1758:. Retrieved
1754:the original
1749:
1734:. Retrieved
1730:the original
1725:
1711:26 September
1709:. Retrieved
1705:the original
1700:
1685:26 September
1683:. Retrieved
1678:
1661:12 September
1659:. Retrieved
1654:
1640:12 September
1638:. Retrieved
1633:
1619:12 September
1617:. Retrieved
1612:
1598:12 September
1596:. Retrieved
1591:
1577:12 September
1575:. Retrieved
1570:
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1265:
1254:. Retrieved
1252:(in Swedish)
1250:www.agero.se
1249:
1239:
1228:. Retrieved
1226:(in Swedish)
1224:www.agero.se
1223:
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1132:Staging area
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775:Ordering or
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607:Link example
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173:Dan Linstedt
166:
162:Dan Linstedt
133:
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41:Please help
36:verification
33:
2272:Spreadsheet
2205:Data mining
1947:Star schema
1024:Methodology
925:Mandatory?
922:Description
761:L_DRIVER_ID
736:Description
658:H_PERSON_ID
631:L_DRIVER_ID
620:Description
518:Description
501:Hub example
328:star schema
269:August 2019
199:star schema
197:Unlike the
2311:Bill Inmon
2115:Degenerate
2084:Fact table
1819:Literature
1784:2014-01-03
1760:2014-01-03
1736:2014-01-03
1256:2023-02-22
1230:2023-02-22
1144:References
1097:Bill Inmon
1085:AutomateDV
1080:Vaultspeed
1075:Wherescape
1052:projects.
739:Mandatory?
705:satellites
699:Satellites
623:Mandatory?
521:Mandatory?
416:confidence
317:normalized
313:Bill Inmon
253:improve it
188:attributes
146:resilience
69:newspapers
2229:Languages
2215:OLAP cube
2200:Dashboard
2151:Transform
2103:Dimension
2058:Data mart
1993:Data mesh
1962:Aggregate
1927:Dimension
1372:, page 83
1208:, page 76
1196:, page 21
1164:, page 74
1149:Citations
1103:Data lake
1038:Six Sigma
919:Fieldname
733:Fieldname
617:Fieldname
515:Fieldname
363:Six Sigma
257:verifying
154:came from
130:Datavault
2379:Category
2344:Products
2188:Concepts
2053:Metadata
2042:Elements
1988:Data hub
1976:Variants
1922:Database
1915:Concepts
1091:See also
895:describe
772:S_SEQ_NR
742:Comment
645:H_CAR_ID
626:Comment
529:H_CAR_ID
524:Comment
428:ontology
420:strength
376:big data
192:parallel
138:database
2294:Related
2146:Extract
2129:Filling
2094:Measure
1504:Sources
1002:Pentaho
947:No (*)
851:No (*)
840:No (*)
829:No (*)
818:No (*)
801:S_LEDTS
390:History
251:Please
83:scholar
2304:People
1538:
1519:
990:Cognos
856:S_RSRC
790:S_LDTS
782:No(**)
671:L_RSRC
554:H_RSRC
85:
78:
71:
64:
56:
2255:Tools
2028:ROLAP
2023:MOLAP
2018:HOLAP
1675:(PDF)
1554:(PDF)
1056:Tools
578:Links
380:NoSQL
299:. In
150:trace
136:is a
90:JSTOR
76:books
2156:Load
2077:Fact
1942:OLAP
1937:Fact
1713:2012
1687:2012
1663:2011
1642:2011
1621:2011
1600:2011
1579:2011
1536:ISBN
1517:ISBN
1034:CMMI
961:The
936:Yes
862:Yes
796:Yes
767:Yes
560:Yes
549:Yes
454:Hubs
418:and
367:SDLC
359:CMMI
142:data
62:news
1030:SEI
963:ETL
873:No
807:No
677:Yes
664:Yes
651:Yes
571:No
355:SEI
295:or
255:by
158:row
132:or
45:by
2381::
1772:.
1748:.
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1438:^
1423:^
1351:^
1248:.
1222:.
1040:,
1000:,
996:,
992:,
753:No
690:No
637:No
535:No
509:.
480:a
473:a
378:,
365:,
361:,
164:.
2051:/
1885:e
1878:t
1871:v
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