1081:, the data used remains in its original locations and real-time access is established to allow analytics across multiple sources creating a virtual data warehouse. This can aid in resolving some technical difficulties such as compatibility problems when combining data from various platforms, lowering the risk of error caused by faulty data, and guaranteeing that the newest data is used. Furthermore, avoiding the creation of a new database containing personal information can make it easier to comply with privacy regulations. However, with data virtualization, the connection to all necessary data sources must be operational as there is no local copy of the data, which is one of the main drawbacks of the approach.
495:. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations, it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as
31:
812:
joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008). The main advantage of this approach is that it is straightforward to add information into the database. Some disadvantages of this approach are that, because of the number of tables involved, it can be difficult for users to join data from different sources into meaningful information and to access the information without a precise understanding of the sources of data and of the
994:. A normal relational database, however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The data warehouse provides a single source of information from which the data marts can read, providing a wide range of business information. The hybrid architecture allows a data warehouse to be replaced with a
777:
into measurements/facts and context/dimensions. Facts are related to the organization's business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008). Another advantage offered by dimensional model is that it does not involve a relational database every time. Thus, this type of modeling technique is very useful for end-user queries in data warehouse.
854:
773:", which are the reference information that gives context to the facts. For example, a sales transaction can be broken up into facts such as the number of products ordered and the total price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.
1009:. The data vault model is not a true third normal form, and breaks some of its rules, but it is a top-down architecture with a bottom up design. The data vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which, when built, still requires the use of a data mart or star schema-based release area for business purposes.
205:
448:). OLAP systems typically have a data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives. The three basic operations in OLAP are Roll-up (Consolidation), Drill-down, and Slicing & Dicing.
507:
populate subject-area databases from data derived from transaction-driven systems to create a storage area where summary data could be further leveraged to inform executive decision-making. This concept served to promote further thinking of how a data warehouse could be developed and managed in a practical way within any enterprise.
149:, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates disparate data sets by transforming the data from the staging layer, often storing this transformed data in an
337:
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 is organized
835:
proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents (even when the same fields are used in both models) but this extra information comes at
1089:
The different methods used to construct/organize a data warehouse specified by an organization are numerous. The hardware utilized, software created and data resources specifically required for the correct functionality of a data warehouse are the main components of the data warehouse architecture.
1068:
In the data warehouse process, data can be aggregated in data marts at different levels of abstraction. The user may start looking at the total sale units of a product in an entire region. Then the user looks at the states in that region. Finally, they may examine the individual stores in a certain
776:
A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly. Dimensional structures are easy to understand for business users, because the structure is divided
222:
tool for data transformation. Instead, it maintains a staging area inside the data warehouse itself. In this approach, data gets extracted from heterogeneous source systems and are then directly loaded into the data warehouse, before any transformation occurs. All necessary transformations are then
811:
that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of
661:
develops and makes public technology known as "textual disambiguation". Textual disambiguation applies context to raw text and reformats the raw text and context into a standard data base format. Once raw text is passed through textual disambiguation, it can easily and efficiently be accessed and
506:
Additionally, with the publication of The IRM Imperative (Wiley & Sons, 1991) by James M. Kerr, the idea of managing and putting a dollar value on an organization's data resources and then reporting that value as an asset on a balance sheet became popular. In the book, Kerr described a way to
1046:
While operational systems reflect current values as they support day-to-day operations, data warehouse data represents a long time horizon (up to 10 years) which means it stores mostly historical data. It is mainly meant for data mining and forecasting. (E.g. if a user is searching for a buying
350:
is a simple form of a data warehouse that is focused on a single subject (or functional area), hence they draw data from a limited number of sources such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. The sources could be
1123:
are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. To improve performance, older data are usually periodically purged from
490:
The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to
351:
internal operational systems, a central data warehouse, or external data. Denormalization is the norm for data modeling techniques in this system. Given that data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement.
87:
1131:, which selects all fields/columns, as is more common in operational databases. Because of these differences in access patterns, operational databases (loosely, OLTP) benefit from the use of a row-oriented DBMS whereas analytics databases (loosely, OLAP) benefit from the use of a
1037:
The data found within the data warehouse is integrated. Since it comes from several operational systems, all inconsistencies must be removed. Consistencies include naming conventions, measurement of variables, encoding structures, physical attributes of data, and so forth.
82:
of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating reports. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.
1135:. Unlike operational systems which maintain a snapshot of the business, data warehouses generally maintain an infinite history which is implemented through ETL processes that periodically migrate data from the operational systems over to the data warehouse.
499:), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "
458:
in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually
662:
analyzed by standard business intelligence technology. Textual disambiguation is accomplished through the execution of textual ETL. Textual disambiguation is useful wherever raw text is found, such as in documents, Hadoop, email, and so forth.
640:, conceived in 1990 as an alternative to Inmon and Kimball to provide long-term historical storage of data coming in from multiple operational systems, with emphasis on tracing, auditing and resilience to change of the source data model.
983:, the information from which is parsed into the actual data warehouse. To reduce data redundancy, larger systems often store the data in a normalized way. Data marts for specific reports can then be built on top of the data warehouse.
647:, along with Derek Strauss and Genia Neushloss, publishes "DW 2.0: The Architecture for the Next Generation of Data Warehousing", explaining his top-down approach to data warehousing and coining the term, data-warehousing 2.0.
951:, that is, data at the greatest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.
440:(OLAP) is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems, response time is an effective measure. OLAP applications are widely used by
477:
future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for
819:
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 (also known as
665:
2013 – Data vault 2.0 was released, having some minor changes to the modeling method, as well as integration with best practices from other methodologies, architectures and implementations including agile and CMMI
1118:
to ensure data integrity. Fully normalized database designs (that is, those satisfying all Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables.
153:(ODS) database. 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
1150:
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
594:
1991 - James M. Kerr authors The IRM Imperative, which suggests data resources could be reported as an asset on a balance sheet, furthering commercial interest in the establishment of data warehouses.
1388:
We can divide IT systems into transactional (OLTP) and analytical (OLAP). In general, we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it.
325:
In regards to source systems listed above, R. Kelly Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases".
188:
are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition of data warehousing includes
1157:
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.
256:
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.
923:. These data marts can then be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of "the bus", a collection of
235:
Integrate data from multiple sources into a single database and data model. More congregation of data to single database so a single query engine can be used to present data in an
1017:
There are basic features that define the data in the data warehouse that include subject orientation, data integration, time-variant, nonvolatile data, and data granularity.
738:
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.
584:
1988 – Barry Devlin and Paul Murphy publish the article "An architecture for a business and information system" where they introduce the term "business data warehouse".
1766:"Information Theory & Business Intelligence Strategy - Small Worlds Data Transformation Measure - MIKE2.0, the open source methodology for Information Development"
824:). These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008).
1163:
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
1055:
The data in the data warehouse is read-only, which means it cannot be updated, created, or deleted (unless there is a regulatory or statutory obligation to do so).
454:(OLTP) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). OLTP systems emphasize very fast query processing and maintaining
1400:
753:
model (Third Normal Form), refers to Bill Inmon's approach in which it is stated that the data warehouse should be modeled using an E-R model/normalized model.
334:
Metadata is data about data. "IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures".
328:
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".
718:
For instance, if there are three BTS in a city, then the facts above can be aggregated from the BTS to the city level in the network dimension. For example:
1005:
components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both third normal form and
794:
It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business.
2372:
1127:
Data warehouses are optimized for analytic access patterns. Analytic access patterns generally involve selecting specific fields and rarely if ever
1210:
Dedić, Nedim; Stanier, Clare (2016). Hammoudi, Slimane; Maciaszek, Leszek; Missikoff, Michele M. Missikoff; Camp, Olivier; Cordeiro, José (eds.).
231:
A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:
556:
2051:
2037:
2023:
2009:
1985:
1749:
1649:
1602:
1242:
629:
1998 – Focal modeling is implemented as an ensemble (hybrid) data warehouse modeling approach, with Patrik Lager as one of the main drivers.
1169:
These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.
1025:
Unlike the operational systems, the data in the data warehouse revolves around the subjects of the enterprise. Subject orientation is not
791:
To maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated.
784:. Where the dimensions are the categorical coordinates in a multi-dimensional cube, the fact is a value corresponding to the coordinates.
1248:
2844:
1349:
581:
and Don
Massaro, releases a hardware/software package and GUI for business users to create a database management and analytic system.
2002:
1941:
1865:
1832:
1574:
893:
338:
by the subject to support decision-makers in the organization. Once data is stored in a data mart or warehouse, it can be accessed.
223:
handled inside the data warehouse itself. Finally, the manipulated data gets loaded into target tables in the same data warehouse.
968:
479:
287:
715:
to extract more service or business-relevant information from it. These are called aggregates or summaries or aggregated facts.
871:
555:(MAintain, Prepare, and Produce Executive Reports), a database management and reporting system that includes the world's first
279:
Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the
687:) receives 1,000 requests for traffic channel allocation, allocates for 820, and rejects the remaining, it would report three
559:. It is the first platform designed for building Information Centers (a forerunner of contemporary data warehouse technology).
2750:
2078:
1617:
1179:
875:
1107:
1029:. Subject orientation can be really useful for decision-making. Gathering the required objects is called subject-oriented.
2804:
2365:
451:
321:
Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes.
2729:
2430:
972:
837:
654:
was formalized in a paper presented at the
International Conference on Conceptual Modeling, and won the best paper award
437:
79:
683:
Facts, as reported by the reporting entity, are said to be at raw level; e.g., in a mobile telephone system, if a BTS (
2455:
960:
189:
1090:
All data warehouses have multiple phases in which the requirements of the organization are modified and fine-tuned.
2839:
2755:
2450:
2415:
1444:
924:
770:
712:
864:
2878:
2708:
2608:
574:
2873:
2629:
2624:
2582:
2358:
2162:
2158:
1404:
1069:
state. Therefore, typically, the analysis starts at a higher level and drills down to lower levels of details.
684:
219:
215:
208:
138:
121:
117:
832:
2698:
2087:
1765:
1640:
Regardt, Olle; Rönnbäck, Lars; Bergholtz, Maria; Johannesson, Paul; Wohed, Petia (2009). "Anchor
Modeling".
928:
615:
1995 – The Data
Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.
492:
30:
246:
systems caused by attempts to run large, long-running analysis queries in transaction processing databases.
184:. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the
2724:
2521:
2499:
2189:
1115:
1103:
1026:
995:
980:
976:
821:
804:
243:
236:
150:
103:
1486:
2816:
2683:
2425:
75:
67:
1889:
Paiho, Satu; Tuominen, Pekka; Rökman, Jyri; Ylikerälä, Markus; Pajula, Juha; Siikavirta, Hanne (2022).
168:, transformed, catalogued, and made available for use by managers and other business professionals for
745:'s approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/
2688:
2603:
2471:
2420:
2319:
1132:
466:
1688:
931:, which are dimensions that are shared (in a specific way) between facts in two or more data marts.
2639:
2577:
2494:
2445:
2259:
2244:
2172:
1973:
1462:
1341:
1120:
1078:
1047:
pattern of a specific customer, the user needs to look at data on the current and past purchases.)
1002:
728:
avg_tch_req_success_city = (tch_req_success_bts1 + tch_req_success_bts2 + tch_req_success_bts3) / 3
637:
470:
444:
techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually
192:, tools to extract, transform, and load data into the repository, and tools to manage and retrieve
1295:
IJCA Proceedings on
International Conference and Workshop on Emerging Trends in Technology (ICWET)
473:
and quantifying hidden patterns in the data using complex mathematical models that can be used to
2071:
1912:
1290:
975:, generating large amounts of data. To consolidate these various data models, and facilitate the
591:, introduces Red Brick Warehouse, a database management system specifically for data warehousing.
280:
99:
1726:
1111:
1225:
Proceedings of the 18th
International Conference on Enterprise Information Systems (ICEIS 2016)
680:
A fact is a value, or measurement, which represents a fact about the managed entity or system.
2551:
2249:
2239:
2103:
2047:
2033:
2019:
2005:
1998:
1981:
1937:
1871:
1861:
1838:
1828:
1745:
1708:
1645:
1598:
1570:
1536:
Devlin, B. A.; Murphy, P. T. (1988). "An architecture for a business and information system".
1345:
1238:
1217:
987:
803:
In the normalized approach, the data in the data warehouse are stored following, to a degree,
750:
519:
460:
270:
86:
1810:
Gartner, Of Data
Warehouses, Operational Data Stores, Data Marts and Data Outhouses, Dec 2005
1511:
2734:
2440:
2199:
2148:
2133:
2113:
2098:
1902:
1700:
1545:
1333:
1228:
920:
766:
181:
146:
1786:
315:
Different architectures for storing data in an organization's data warehouse or data marts;
2634:
2598:
2537:
2489:
2329:
2264:
2254:
2224:
2167:
2138:
2128:
991:
651:
185:
177:
1334:
1218:
International
Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy
723:
tch_req_success_city = tch_req_success_bts1 + tch_req_success_bts2 + tch_req_success_bts3
1365:
2339:
2299:
2279:
2274:
2229:
2204:
2123:
1564:
1099:
813:
455:
165:
142:
107:
312:
Data integration technology and processes that are needed to prepare the data for use;
2867:
2811:
2556:
2304:
2294:
2269:
2143:
2108:
2064:
1916:
964:
742:
619:
588:
548:
515:
496:
71:
1336:
Introduction to
Information Systems: Enabling and Transforming Business, 4th Edition
2823:
2760:
2644:
2314:
2309:
2289:
2284:
2214:
2209:
2184:
2177:
2153:
1291:"Optimization of Data Warehousing System: Simplification in Reporting and Analysis"
948:
633:
578:
263:, by providing consistent codes and descriptions, flagging or even fixing bad data.
260:
157:
and aggregate facts. The combination of facts and dimensions is sometimes called a
111:
1931:
1629:
Data Modeling Meetup Munich: An Introduction to Focal with Patrik Lager - YouTube
331:
Rainer discusses storing data in an organization's data warehouse or data marts.
2693:
2435:
2234:
2194:
1379:
1213:
An Evaluation of the Challenges of Multilingualism in Data Warehouse Development
1006:
919:
are first created to provide reporting and analytical capabilities for specific
853:
769:
is partitioned into "facts", which are generally numeric transaction data, and "
762:
746:
445:
441:
169:
158:
17:
836:
the cost of usability. The technique measures information quantity in terms of
601:, introduces Prism Warehouse Manager, software for developing a data warehouse.
2799:
2572:
1995:
1957:
1704:
1628:
944:
658:
644:
605:
598:
541:
474:
250:
1842:
1712:
1233:
2703:
2546:
2481:
2324:
2219:
1875:
1190:
1184:
916:
781:
534:
500:
431:
347:
43:
35:
1827:. Ponniah, Paulraj. (2nd ed.). Hoboken, N.J.: John Wiley & Sons.
711:
Facts at the raw level are further aggregated to higher levels in various
463:). Normalization is the norm for data modeling techniques in this system.
2541:
2476:
2410:
2118:
1265:
567:
563:
193:
124:(ELT) are the two main approaches used to build a data warehouse system.
1549:
1437:
840:
and usability in terms of the Small Worlds data transformation measure.
204:
1907:
1890:
1642:
Proceedings of the 28th International Conference on Conceptual Modeling
878: in this section. Unsourced material may be challenged and removed.
1143:
These terms refer to the level of sophistication of a data warehouse:
787:
The main disadvantages of the dimensional approach are the following:
305:
The environment for data warehouses and marts includes the following:
1992:
Data Warehouse Implementations: Critical Implementation Factors Study
1187: – System or repository of data stored in its natural/raw format
552:
95:
1689:"The dimensional fact model: a conceptual model for data warehouses"
1211:
998:
repository where operational (not static) information could reside.
242:
Mitigate the problem of database isolation level lock contention in
986:
A hybrid (also called ensemble) data warehouse database is kept on
2516:
2511:
2506:
2350:
276:
Restructure the data so that it makes sense to the business users.
85:
29:
1266:"What is a Data Warehouse? | Key Concepts | Amazon Web Services"
173:
2781:
2665:
2392:
2354:
2060:
1102:
and speed of recording of business transactions through use of
1687:
Golfarelli, Matteo; Maio, Dario; Rizzi, Stefano (1998-06-01).
1193: – Distributed architecture framework for data management
847:
780:
The model of facts and dimensions can also be understood as a
570:
database computer specifically designed for decision support.
1289:
Patil, Preeti S.; Srikantha Rao; Suryakant B. Patil (2011).
102:(such as marketing or sales). The data may pass through an
1098:
Operational systems are optimized for the preservation of
734:
Dimensional versus normalized approach for storage of data
309:
Source systems that provide data to the warehouse or mart;
273:
for all data of interest regardless of the data's source.
1693:
International Journal of Cooperative Information Systems
537:
and IRI provide dimensional data marts for retail sales.
318:
Different tools and applications for a variety of users;
286:
Add value to operational business applications, notably
114:
before it is used in the data warehouse for reporting.
2056:
1891:"Opportunities of collected city data for smart cities"
1644:. ER '09. Gramado, Brazil: Springer-Verlag: 234–250.
544:
begins to define and discuss the term Data Warehouse.
510:
Key developments in early years of data warehousing:
1332:
Rainer, R. Kelly; Cegielski, Casey G. (2012-05-01).
266:
Present the organization's information consistently.
2832:
2792:
2743:
2717:
2676:
2617:
2591:
2565:
2530:
2464:
2403:
1787:"The Bottom-Up Misnomer - DecisionWorks Consulting"
1128:
943:approach is designed using a normalized enterprise
727:
722:
705:
700:
695:
1978:Competing on Analytics: The New Science of Winning
1825:Data warehousing fundamentals for IT professionals
1487:"Data Warehouse – What It Is & Why It Matters"
522:, in a joint research project, develop the terms
38:overview, with Data Marts shown in the top right.
1110:. Operational system designers generally follow
253:, even if the source transaction systems do not.
503:" that was tailored for ready access by users.
218:-based data warehousing gets rid of a separate
979:process, data warehouses often make use of an
293:Make decision–support queries easier to write.
161:. The access layer helps users retrieve data.
2845:Data warehousing products and their producers
2366:
2072:
1860:(4th ed.). Indianapolis, IN: Wiley Pub.
1227:. Vol. 1. SciTePress. pp. 196–206.
8:
1930:Gupta, Satinder Bal; Mittal, Aditya (2009).
1327:
1325:
1323:
1321:
1319:
1317:
1438:"The audit of the Data Warehouse Framework"
1423:
1421:
1301:(6). Foundation of Computer Science: 33–37.
749:. The normalized approach, also called the
2789:
2778:
2673:
2662:
2400:
2389:
2373:
2359:
2351:
2079:
2065:
2057:
1933:Introduction to Database Management System
296:Organize and disambiguate repetitive data.
90:The basic architecture of a data warehouse
1906:
1232:
894:Learn how and when to remove this message
691:or measurements to a management system:
353:
203:
1202:
1980:(2007) Harvard Business School Press.
1180:List of business intelligence software
807:rules. Tables are grouped together by
434:, independent, and hybrid data marts.
355:Difference between data warehouse and
74:and is considered a core component of
1818:
1816:
1588:
1586:
1463:"A Short History of Data Warehousing"
587:1990 – Red Brick Systems, founded by
7:
2032:Second Edition (2010) Dan linstedt,
967:feeding the warehouse often include
876:adding citations to reliable sources
110:for additional operations to ensure
94:The data stored in the warehouse is
2030:The Business of Data Vault Modeling
1618:Introduction to the focal framework
1340:(Kindle ed.). Wiley. pp.
959:Data warehouses often resemble the
741:The dimensional approach refers to
597:1991 – Prism Solutions, founded by
2730:MultiDimensional eXpressions (MDX)
1147:Offline operational data warehouse
636:releases in the public domain the
211:-based data warehouse architecture
25:
1510:Paul Gillin (February 20, 1984).
2014:Kimball, Ralph and Ross, Margy.
1512:"Will Teradata revive a market?"
1450:from the original on 2012-05-12.
1254:from the original on 2018-05-22.
969:customer relationship management
852:
480:customer relationship management
288:customer relationship management
141:(ETL)-based data warehouse uses
27:Centralized storage of knowledge
1665:A short intro to #datavault 2.0
863:needs additional citations for
164:The main source of the data is
2751:Business intelligence software
2630:Extract, load, transform (ELT)
2625:Extract, transform, load (ETL)
2028:Linstedt, Graziano, Hultgren.
1676:Data Vault 2.0 Being Announced
78:. Data warehouses are central
1:
2699:Decision support system (DSS)
1461:Kempe, Shannon (2012-08-23).
1139:Evolution in organization use
493:decision support environments
452:Online transaction processing
407:How much time takes to build
2725:Data Mining Extensions (DMX)
2046:(2005) John Wiley and Sons,
2018:Third Edition (2013) Wiley,
1727:"Introduction to Data Cubes"
1675:
1403:. 2002-04-15. Archived from
973:enterprise resource planning
438:Online analytical processing
430:Types of data marts include
174:online analytical processing
2486:Ensemble modeling patterns
2456:Single version of the truth
2044:Building the Data Warehouse
1858:Building the data warehouse
1768:. Mike2.openmethodology.org
1742:Information-Driven Business
1566:Building the Data Warehouse
961:hub and spokes architecture
829:Information-Driven Business
610:Building the Data Warehouse
190:business intelligence tools
2895:
2840:Comparison of OLAP servers
2016:The Data Warehouse Toolkit
1856:Inmon, William H. (2005).
1823:Paulraj., Ponniah (2010).
1664:
1595:The Data Warehouse Toolkit
1311:Marakas & O'Brien 2009
624:The Data Warehouse Toolkit
200:ELT-based data warehousing
154:
133:ETL-based data warehousing
2788:
2777:
2709:Data warehouse automation
2672:
2661:
2399:
2394:Creating a data warehouse
2388:
2094:
1705:10.1142/S0218843098000118
1166:Integrated data warehouse
1108:entity–relationship model
1094:Versus operational system
575:Metaphor Computer Systems
60:enterprise data warehouse
1791:DecisionWorks Consulting
1740:Hillard, Robert (2010).
1270:Amazon Web Services, Inc
1234:10.5220/0005858401960206
685:base transceiver station
385:Number of subject areas
139:extract, transform, load
122:extract, load, transform
118:Extract, transform, load
66:), is a system used for
2735:XML for Analysis (XMLA)
1593:Kimball, Ralph (2011).
816:of the data warehouse.
396:How difficult to build
2667:Using a data warehouse
2522:Operational data store
1976:and Harris, Jeanne G.
1936:. Laxmi Publications.
1597:. Wiley. p. 237.
1344:, 128, 130, 131, 133.
1160:On-time data warehouse
1154:Offline data warehouse
1116:database normalization
1104:database normalization
1027:database normalization
996:master data management
981:operational data store
977:extract transform load
805:database normalization
244:transaction processing
212:
151:operational data store
104:operational data store
91:
39:
2684:Business intelligence
1124:operational systems.
701:tch_req_success = 820
207:
89:
76:business intelligence
33:
2500:Focal point modeling
2472:Column-oriented DBMS
2421:Dimensional modeling
2260:Protection (privacy)
1974:Davenport, Thomas H.
1563:Inmon, Bill (1992).
1427:Kimball 2013, pg. 15
1384:Datawarehouse4u.Info
1366:"Data Mart Concepts"
1133:column-oriented DBMS
1121:Relational databases
925:conformed dimensions
872:improve this article
763:dimensional approach
757:Dimensional approach
696:tch_req_total = 1000
467:Predictive analytics
58:), also known as an
2805:Information factory
2578:Early-arriving fact
2495:Data vault modeling
2446:Reverse star schema
1793:. 17 September 2003
1550:10.1147/sj.271.0060
1538:IBM Systems Journal
1079:data virtualization
1003:data vault modeling
838:information entropy
799:Normalized approach
671:Information storage
638:Data vault modeling
622:publishes the book
608:publishes the book
359:
281:operational systems
100:operational systems
34:Data Warehouse and
2756:Reporting software
1908:10.1049/smc2.12044
1401:"The Story So Far"
921:business processes
706:tch_req_fail = 180
374:Scope of the data
354:
213:
92:
40:
2861:
2860:
2857:
2856:
2853:
2852:
2773:
2772:
2769:
2768:
2657:
2656:
2653:
2652:
2552:Sixth normal form
2348:
2347:
2340:Wrangling/munging
2190:Format management
2052:978-81-265-0645-3
2038:978-1-4357-1914-9
2024:978-1-118-53080-1
2010:978-3-639-18589-8
1990:Ganczarski, Joe.
1986:978-1-4221-0332-6
1751:978-0-470-62577-4
1699:(2n03): 215–247.
1651:978-3-642-04839-5
1604:978-0-470-14977-5
1518:. pp. 43, 48
1244:978-989-758-187-8
988:third normal form
904:
903:
896:
520:Dartmouth College
428:
427:
418:Amount of memory
271:common data model
269:Provide a single
16:(Redirected from
2886:
2879:Data warehousing
2790:
2779:
2674:
2663:
2441:Snowflake schema
2401:
2390:
2375:
2368:
2361:
2352:
2081:
2074:
2067:
2058:
1962:
1961:
1958:"Data Warehouse"
1954:
1948:
1947:
1927:
1921:
1920:
1910:
1895:IET Smart Cities
1886:
1880:
1879:
1853:
1847:
1846:
1820:
1811:
1808:
1802:
1801:
1799:
1798:
1783:
1777:
1776:
1774:
1773:
1762:
1756:
1755:
1737:
1731:
1730:
1723:
1717:
1716:
1684:
1678:
1673:
1667:
1662:
1656:
1655:
1637:
1631:
1626:
1620:
1615:
1609:
1608:
1590:
1581:
1580:
1560:
1554:
1553:
1533:
1527:
1526:
1524:
1523:
1507:
1501:
1500:
1498:
1497:
1483:
1477:
1476:
1474:
1473:
1458:
1452:
1451:
1449:
1442:
1434:
1428:
1425:
1416:
1415:
1413:
1412:
1397:
1391:
1390:
1376:
1370:
1369:
1362:
1356:
1355:
1339:
1329:
1312:
1309:
1303:
1302:
1286:
1280:
1279:
1277:
1276:
1262:
1256:
1255:
1253:
1236:
1222:
1207:
1130:
1021:Subject-oriented
907:Bottom-up design
899:
892:
888:
885:
879:
856:
848:
767:transaction data
729:
724:
707:
702:
697:
380:department-wide
377:enterprise-wide
360:
358:
182:decision support
147:data integration
106:and may require
21:
18:Data warehousing
2894:
2893:
2889:
2888:
2887:
2885:
2884:
2883:
2874:Data management
2864:
2863:
2862:
2849:
2828:
2784:
2765:
2739:
2713:
2668:
2649:
2613:
2609:Slowly changing
2599:Dimension table
2587:
2561:
2538:Data dictionary
2526:
2490:Anchor modeling
2460:
2395:
2384:
2382:Data warehouses
2379:
2349:
2344:
2320:Synchronization
2090:
2085:
2042:William Inmon.
1970:
1968:Further reading
1965:
1960:. 6 April 2019.
1956:
1955:
1951:
1944:
1929:
1928:
1924:
1888:
1887:
1883:
1868:
1855:
1854:
1850:
1835:
1822:
1821:
1814:
1809:
1805:
1796:
1794:
1785:
1784:
1780:
1771:
1769:
1764:
1763:
1759:
1752:
1739:
1738:
1734:
1725:
1724:
1720:
1686:
1685:
1681:
1674:
1670:
1663:
1659:
1652:
1639:
1638:
1634:
1627:
1623:
1616:
1612:
1605:
1592:
1591:
1584:
1577:
1562:
1561:
1557:
1535:
1534:
1530:
1521:
1519:
1509:
1508:
1504:
1495:
1493:
1485:
1484:
1480:
1471:
1469:
1460:
1459:
1455:
1447:
1440:
1436:
1435:
1431:
1426:
1419:
1410:
1408:
1399:
1398:
1394:
1380:"OLTP vs. OLAP"
1378:
1377:
1373:
1368:. Oracle. 2007.
1364:
1363:
1359:
1352:
1331:
1330:
1315:
1310:
1306:
1288:
1287:
1283:
1274:
1272:
1264:
1263:
1259:
1251:
1245:
1220:
1209:
1208:
1204:
1200:
1176:
1141:
1112:Codd's 12 rules
1096:
1087:
1075:
1066:
1061:
1053:
1044:
1035:
1023:
1015:
1013:Characteristics
992:data redundancy
957:
937:
935:Top-down design
929:conformed facts
909:
900:
889:
883:
880:
869:
857:
846:
801:
759:
736:
678:
673:
652:Anchor modeling
566:introduces the
488:
366:Data warehouse
356:
344:
342:Related systems
303:
229:
202:
186:data dictionary
178:market research
135:
130:
28:
23:
22:
15:
12:
11:
5:
2892:
2890:
2882:
2881:
2876:
2866:
2865:
2859:
2858:
2855:
2854:
2851:
2850:
2848:
2847:
2842:
2836:
2834:
2830:
2829:
2827:
2826:
2821:
2820:
2819:
2817:Enterprise bus
2809:
2808:
2807:
2796:
2794:
2786:
2785:
2782:
2775:
2774:
2771:
2770:
2767:
2766:
2764:
2763:
2758:
2753:
2747:
2745:
2741:
2740:
2738:
2737:
2732:
2727:
2721:
2719:
2715:
2714:
2712:
2711:
2706:
2701:
2696:
2691:
2686:
2680:
2678:
2670:
2669:
2666:
2659:
2658:
2655:
2654:
2651:
2650:
2648:
2647:
2642:
2637:
2632:
2627:
2621:
2619:
2615:
2614:
2612:
2611:
2606:
2601:
2595:
2593:
2589:
2588:
2586:
2585:
2580:
2575:
2569:
2567:
2563:
2562:
2560:
2559:
2554:
2549:
2544:
2534:
2532:
2528:
2527:
2525:
2524:
2519:
2514:
2509:
2504:
2503:
2502:
2497:
2492:
2484:
2479:
2474:
2468:
2466:
2462:
2461:
2459:
2458:
2453:
2448:
2443:
2438:
2433:
2428:
2423:
2418:
2413:
2407:
2405:
2397:
2396:
2393:
2386:
2385:
2380:
2378:
2377:
2370:
2363:
2355:
2346:
2345:
2343:
2342:
2337:
2332:
2327:
2322:
2317:
2312:
2307:
2302:
2297:
2292:
2287:
2282:
2277:
2272:
2267:
2262:
2257:
2252:
2247:
2245:Pre-processing
2242:
2237:
2232:
2227:
2222:
2217:
2212:
2207:
2202:
2197:
2192:
2187:
2182:
2181:
2180:
2175:
2170:
2156:
2151:
2146:
2141:
2136:
2131:
2126:
2121:
2116:
2111:
2106:
2101:
2095:
2092:
2091:
2086:
2084:
2083:
2076:
2069:
2061:
2055:
2054:
2040:
2026:
2012:
1988:
1969:
1966:
1964:
1963:
1949:
1942:
1922:
1901:(4): 275–291.
1881:
1866:
1848:
1833:
1812:
1803:
1778:
1757:
1750:
1732:
1718:
1679:
1668:
1657:
1650:
1632:
1621:
1610:
1603:
1582:
1575:
1555:
1528:
1516:Computer World
1502:
1478:
1453:
1429:
1417:
1392:
1371:
1357:
1351:978-1118129401
1350:
1313:
1304:
1281:
1257:
1243:
1201:
1199:
1196:
1195:
1194:
1188:
1182:
1175:
1172:
1171:
1170:
1167:
1164:
1161:
1158:
1155:
1152:
1148:
1140:
1137:
1100:data integrity
1095:
1092:
1086:
1083:
1074:
1073:Virtualization
1071:
1065:
1062:
1060:
1057:
1052:
1049:
1043:
1040:
1034:
1031:
1022:
1019:
1014:
1011:
965:Legacy systems
956:
953:
936:
933:
908:
905:
902:
901:
860:
858:
851:
845:
844:Design methods
842:
833:Robert Hillard
814:data structure
800:
797:
796:
795:
792:
758:
755:
735:
732:
731:
730:
725:
709:
708:
703:
698:
677:
674:
672:
669:
668:
667:
663:
655:
648:
641:
630:
627:
616:
613:
602:
595:
592:
585:
582:
571:
560:
545:
538:
531:
497:legacy systems
487:
484:
456:data integrity
426:
425:
422:
419:
415:
414:
411:
408:
404:
403:
400:
397:
393:
392:
389:
386:
382:
381:
378:
375:
371:
370:
367:
364:
343:
340:
323:
322:
319:
316:
313:
310:
302:
299:
298:
297:
294:
291:
290:(CRM) systems.
284:
277:
274:
267:
264:
257:
254:
247:
240:
228:
225:
201:
198:
134:
131:
129:
126:
108:data cleansing
48:data warehouse
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
2891:
2880:
2877:
2875:
2872:
2871:
2869:
2846:
2843:
2841:
2838:
2837:
2835:
2831:
2825:
2822:
2818:
2815:
2814:
2813:
2812:Ralph Kimball
2810:
2806:
2803:
2802:
2801:
2798:
2797:
2795:
2791:
2787:
2780:
2776:
2762:
2759:
2757:
2754:
2752:
2749:
2748:
2746:
2742:
2736:
2733:
2731:
2728:
2726:
2723:
2722:
2720:
2716:
2710:
2707:
2705:
2702:
2700:
2697:
2695:
2692:
2690:
2687:
2685:
2682:
2681:
2679:
2675:
2671:
2664:
2660:
2646:
2643:
2641:
2638:
2636:
2633:
2631:
2628:
2626:
2623:
2622:
2620:
2616:
2610:
2607:
2605:
2602:
2600:
2597:
2596:
2594:
2590:
2584:
2581:
2579:
2576:
2574:
2571:
2570:
2568:
2564:
2558:
2557:Surrogate key
2555:
2553:
2550:
2548:
2545:
2543:
2539:
2536:
2535:
2533:
2529:
2523:
2520:
2518:
2515:
2513:
2510:
2508:
2505:
2501:
2498:
2496:
2493:
2491:
2488:
2487:
2485:
2483:
2480:
2478:
2475:
2473:
2470:
2469:
2467:
2463:
2457:
2454:
2452:
2449:
2447:
2444:
2442:
2439:
2437:
2434:
2432:
2429:
2427:
2424:
2422:
2419:
2417:
2414:
2412:
2409:
2408:
2406:
2402:
2398:
2391:
2387:
2383:
2376:
2371:
2369:
2364:
2362:
2357:
2356:
2353:
2341:
2338:
2336:
2333:
2331:
2328:
2326:
2323:
2321:
2318:
2316:
2313:
2311:
2308:
2306:
2303:
2301:
2298:
2296:
2293:
2291:
2288:
2286:
2283:
2281:
2278:
2276:
2273:
2271:
2268:
2266:
2263:
2261:
2258:
2256:
2253:
2251:
2248:
2246:
2243:
2241:
2238:
2236:
2233:
2231:
2228:
2226:
2223:
2221:
2218:
2216:
2213:
2211:
2208:
2206:
2203:
2201:
2198:
2196:
2193:
2191:
2188:
2186:
2183:
2179:
2176:
2174:
2171:
2169:
2166:
2165:
2164:
2160:
2157:
2155:
2152:
2150:
2147:
2145:
2142:
2140:
2137:
2135:
2132:
2130:
2127:
2125:
2122:
2120:
2117:
2115:
2112:
2110:
2107:
2105:
2102:
2100:
2097:
2096:
2093:
2089:
2082:
2077:
2075:
2070:
2068:
2063:
2062:
2059:
2053:
2049:
2045:
2041:
2039:
2035:
2031:
2027:
2025:
2021:
2017:
2013:
2011:
2007:
2004:
2003:3-639-18589-7
2000:
1997:
1993:
1989:
1987:
1983:
1979:
1975:
1972:
1971:
1967:
1959:
1953:
1950:
1945:
1943:9788131807248
1939:
1935:
1934:
1926:
1923:
1918:
1914:
1909:
1904:
1900:
1896:
1892:
1885:
1882:
1877:
1873:
1869:
1867:9780764599446
1863:
1859:
1852:
1849:
1844:
1840:
1836:
1834:9780470462072
1830:
1826:
1819:
1817:
1813:
1807:
1804:
1792:
1788:
1782:
1779:
1767:
1761:
1758:
1753:
1747:
1743:
1736:
1733:
1728:
1722:
1719:
1714:
1710:
1706:
1702:
1698:
1694:
1690:
1683:
1680:
1677:
1672:
1669:
1666:
1661:
1658:
1653:
1647:
1643:
1636:
1633:
1630:
1625:
1622:
1619:
1614:
1611:
1606:
1600:
1596:
1589:
1587:
1583:
1578:
1576:0-471-56960-7
1572:
1568:
1567:
1559:
1556:
1551:
1547:
1543:
1539:
1532:
1529:
1517:
1513:
1506:
1503:
1492:
1488:
1482:
1479:
1468:
1464:
1457:
1454:
1446:
1439:
1433:
1430:
1424:
1422:
1418:
1407:on 2008-07-08
1406:
1402:
1396:
1393:
1389:
1385:
1381:
1375:
1372:
1367:
1361:
1358:
1353:
1347:
1343:
1338:
1337:
1328:
1326:
1324:
1322:
1320:
1318:
1314:
1308:
1305:
1300:
1296:
1292:
1285:
1282:
1271:
1267:
1261:
1258:
1250:
1246:
1240:
1235:
1230:
1226:
1219:
1215:
1214:
1206:
1203:
1197:
1192:
1189:
1186:
1183:
1181:
1178:
1177:
1173:
1168:
1165:
1162:
1159:
1156:
1153:
1149:
1146:
1145:
1144:
1138:
1136:
1134:
1125:
1122:
1117:
1113:
1109:
1105:
1101:
1093:
1091:
1084:
1082:
1080:
1072:
1070:
1063:
1058:
1056:
1050:
1048:
1041:
1039:
1032:
1030:
1028:
1020:
1018:
1012:
1010:
1008:
1004:
999:
997:
993:
990:to eliminate
989:
984:
982:
978:
974:
970:
966:
962:
955:Hybrid design
954:
952:
950:
949:"Atomic" data
946:
942:
934:
932:
930:
926:
922:
918:
914:
906:
898:
895:
887:
877:
873:
867:
866:
861:This section
859:
855:
850:
849:
843:
841:
839:
834:
830:
825:
823:
817:
815:
810:
809:subject areas
806:
798:
793:
790:
789:
788:
785:
783:
778:
774:
772:
768:
764:
756:
754:
752:
748:
744:
743:Ralph Kimball
739:
733:
726:
721:
720:
719:
716:
714:
704:
699:
694:
693:
692:
690:
686:
681:
675:
670:
664:
660:
656:
653:
649:
646:
642:
639:
635:
631:
628:
625:
621:
620:Ralph Kimball
617:
614:
611:
607:
603:
600:
596:
593:
590:
589:Ralph Kimball
586:
583:
580:
577:, founded by
576:
572:
569:
565:
561:
558:
554:
550:
549:Sperry Univac
546:
543:
539:
536:
532:
529:
525:
521:
517:
516:General Mills
513:
512:
511:
508:
504:
502:
498:
494:
485:
483:
481:
476:
472:
468:
464:
462:
457:
453:
449:
447:
443:
439:
435:
433:
423:
420:
417:
416:
412:
409:
406:
405:
401:
398:
395:
394:
390:
387:
384:
383:
379:
376:
373:
372:
368:
365:
362:
361:
352:
349:
341:
339:
335:
332:
329:
326:
320:
317:
314:
311:
308:
307:
306:
300:
295:
292:
289:
285:
282:
278:
275:
272:
268:
265:
262:
258:
255:
252:
248:
245:
241:
238:
234:
233:
232:
226:
224:
221:
217:
210:
206:
199:
197:
195:
191:
187:
183:
179:
175:
171:
167:
162:
160:
156:
152:
148:
144:
140:
132:
127:
125:
123:
119:
115:
113:
109:
105:
101:
97:
88:
84:
81:
77:
73:
72:data analysis
69:
65:
61:
57:
53:
49:
45:
37:
32:
19:
2824:Dan Linstedt
2381:
2334:
2250:Preservation
2240:Philanthropy
2104:Augmentation
2043:
2029:
2015:
1991:
1977:
1952:
1932:
1925:
1898:
1894:
1884:
1857:
1851:
1824:
1806:
1795:. Retrieved
1790:
1781:
1770:. Retrieved
1760:
1741:
1735:
1721:
1696:
1692:
1682:
1671:
1660:
1641:
1635:
1624:
1613:
1594:
1565:
1558:
1541:
1537:
1531:
1520:. Retrieved
1515:
1505:
1494:. Retrieved
1490:
1481:
1470:. Retrieved
1466:
1456:
1432:
1409:. Retrieved
1405:the original
1395:
1387:
1383:
1374:
1360:
1335:
1307:
1298:
1294:
1284:
1273:. Retrieved
1269:
1260:
1224:
1212:
1205:
1142:
1126:
1097:
1088:
1085:Architecture
1076:
1067:
1054:
1045:
1042:Time-variant
1036:
1024:
1016:
1000:
985:
958:
940:
938:
912:
910:
890:
881:
870:Please help
865:verification
862:
828:
826:
822:Normal Forms
818:
808:
802:
786:
779:
775:
760:
740:
737:
717:
710:
688:
682:
679:
634:Dan Linstedt
623:
609:
579:David Liddle
527:
523:
509:
505:
489:
465:
450:
446:star schemas
436:
429:
345:
336:
333:
330:
327:
324:
304:
261:data quality
251:data history
230:
214:
163:
137:The typical
136:
116:
112:data quality
93:
80:repositories
63:
59:
55:
51:
47:
41:
2761:Spreadsheet
2694:Data mining
2436:Star schema
2310:Stewardship
2200:Integration
2149:Degradation
2134:Compression
2114:Archaeology
2099:Acquisition
1491:www.sas.com
1467:DATAVERSITY
1064:Aggregation
1051:Nonvolatile
1007:star schema
747:star schema
551:introduces
442:Data Mining
170:data mining
159:star schema
2868:Categories
2800:Bill Inmon
2604:Degenerate
2573:Fact table
2330:Validation
2265:Publishing
2255:Processing
2225:Management
2139:Corruption
2129:Collection
1996:VDM Verlag
1797:2016-03-06
1772:2013-06-14
1522:2017-03-13
1496:2024-05-10
1472:2024-05-10
1411:2008-09-21
1275:2023-02-13
1198:References
1033:Integrated
945:data model
917:data marts
915:approach,
771:dimensions
713:dimensions
666:principles
659:Bill Inmon
645:Bill Inmon
606:Bill Inmon
599:Bill Inmon
542:Bill Inmon
524:dimensions
501:data marts
399:difficult
369:Data mart
363:Attribute
120:(ETL) and
2718:Languages
2704:OLAP cube
2689:Dashboard
2640:Transform
2592:Dimension
2547:Data mart
2482:Data mesh
2451:Aggregate
2416:Dimension
2335:Warehouse
2300:Scrubbing
2280:Retention
2275:Reduction
2230:Migration
2205:Integrity
2173:Transform
2124:Cleansing
1917:253467923
1843:662453070
1744:. Wiley.
1713:0218-8430
1569:. Wiley.
1544:: 60–80.
1191:Data mesh
1185:Data lake
1151:database.
913:bottom-up
884:July 2015
782:data cube
535:ACNielsen
469:is about
432:dependent
388:multiple
357:data mart
348:data mart
249:Maintain
98:from the
68:reporting
44:computing
36:Data mart
2833:Products
2677:Concepts
2542:Metadata
2531:Elements
2477:Data hub
2465:Variants
2411:Database
2404:Concepts
2305:Security
2295:Scraping
2270:Recovery
2144:Curation
2109:Analysis
1876:61762085
1445:Archived
1386:. 2009.
1249:Archived
1174:See also
1129:select *
941:top-down
568:DBC/1012
564:Teradata
540:1970s –
533:1970s –
514:1960s –
424:limited
259:Improve
227:Benefits
194:metadata
166:cleansed
128:Variants
96:uploaded
2783:Related
2635:Extract
2618:Filling
2583:Measure
2315:Storage
2290:Science
2285:Quality
2215:Lineage
2210:Library
2185:Farming
2168:Extract
2154:Editing
1994:(2009)
1106:and an
1059:Options
911:In the
657:2012 –
650:2008 –
643:2008 –
632:2000 –
618:1996 –
604:1992 –
573:1984 –
562:1983 –
547:1975 –
486:History
482:(CRM).
475:predict
471:finding
421:larger
391:single
301:Generic
143:staging
2793:People
2235:Mining
2195:Fusion
2050:
2036:
2022:
2008:
2001:
1984:
1940:
1915:
1874:
1864:
1841:
1831:
1748:
1711:
1648:
1601:
1573:
1348:
1241:
553:MAPPER
2744:Tools
2517:ROLAP
2512:MOLAP
2507:HOLAP
1913:S2CID
1448:(PDF)
1441:(PDF)
1252:(PDF)
1221:(PDF)
1077:With
761:In a
689:facts
676:Facts
528:facts
413:less
410:more
402:easy
155:facts
2645:Load
2566:Fact
2431:OLAP
2426:Fact
2325:Type
2220:Loss
2178:Load
2088:Data
2048:ISBN
2034:ISBN
2020:ISBN
2006:ISBN
1999:ISBN
1982:ISBN
1938:ISBN
1872:OCLC
1862:ISBN
1839:OCLC
1829:ISBN
1746:ISBN
1709:ISSN
1646:ISBN
1599:ISBN
1571:ISBN
1346:ISBN
1239:ISBN
1001:The
971:and
939:The
927:and
526:and
518:and
180:and
70:and
46:, a
2163:ELT
2159:ETL
2119:Big
1903:doi
1701:doi
1546:doi
1342:127
1229:doi
1114:of
874:by
827:In
751:3NF
557:4GL
461:3NF
237:ODS
220:ETL
216:ELT
209:ELT
64:EDW
56:DWH
54:or
42:In
2870::
1911:.
1897:.
1893:.
1870:.
1837:.
1815:^
1789:.
1707:.
1697:07
1695:.
1691:.
1585:^
1542:27
1540:.
1514:.
1489:.
1465:.
1443:.
1420:^
1382:.
1316:^
1297:.
1293:.
1268:.
1247:.
1237:.
1223:.
1216:.
963:.
947:.
831:,
765:,
346:A
196:.
176:,
172:,
145:,
52:DW
2540:/
2374:e
2367:t
2360:v
2161:/
2080:e
2073:t
2066:v
1946:.
1919:.
1905::
1899:4
1878:.
1845:.
1800:.
1775:.
1754:.
1729:.
1715:.
1703::
1654:.
1607:.
1579:.
1552:.
1548::
1525:.
1499:.
1475:.
1414:.
1354:.
1299:9
1278:.
1231::
897:)
891:(
886:)
882:(
868:.
626:.
612:.
530:.
283:.
239:.
62:(
50:(
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
