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[[File:Data warehouse overview.JPG|thumb|upright=1.5|DataPregled warehouseskladišta overviewpodataka]]
[[File:Data warehouse architecture.jpg|thumb|upright=1.5|TheOsnovna basicarhitektura architectureskladišta of a data warehousepodataka]]
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U [[computing|računarstvu]], '''skladištenje podataka''' ({{jez-eng-lat|data warehouse}}, -{'''DW'''}- ili -{'''DWH'''}-), also known as an '''enterprise data warehouse''' ('''EDW'''), is a system used for [[Business reporting|reporting]] and [[data analysis]], and is considered a core component of [[business intelligence]].<ref>{{cite conference|last1=Dedić|first1=Nedim|last2=Stanier|first2=Clare|year=2016|editor1-last=Hammoudi|editor1-first=Slimane|editor2-last=Maciaszek|editor2-first=Leszek|editor3-last=Missikoff|editor3-first=Michele M. Missikoff|editor4-last=Camp|editor4-first=Olivier|editor5-last=Cordeiro|editor5-first=José|title=An Evaluation of the Challenges of Multilingualism in Data Warehouse Development|url=http://eprints.staffs.ac.uk/2770/|journal=Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)|publisher=SciTePress|volume=1|pages=196–206|conference=International Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy|conferenceurl=https://eprints.staffs.ac.uk/2770/1/ICEIS_2016_Volume_1.pdf|doi=10.5220/0005858401960206|isbn=978-989-758-187-8}}</ref> DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place<ref name="rjmetrics">{{cite web|url=https://blog.rjmetrics.com/2014/12/04/10-common-mistakes-when-building-a-data-warehouse/|publisher=blog.rjmetrics.com|title=9 Reasons Data Warehouse Projects Fail|accessdate=2017-04-30}}</ref> that are used for creating analytical reports for workers throughout the enterprise.<ref name="spotlessdata">{{cite web|url=https://web.archive.org/web/20180726071809/https://spotlessdata.com/blog/exploring-data-warehouses-and-data-quality|publisher=spotlessdata.com|title=Exploring Data Warehouses and Data Quality|accessdate=2017-04-30}}</ref> ▼
▲U [[computing|računarstvu]], '''skladištenje podataka''' ({{jez-eng-lat|data warehouse}}, -{'''DW'''}- ili -{'''DWH'''}-), alsotakođe knownpoznatо as ankao '' 'enterpriseposlovno dataskladište warehouse'podataka'' ( '''EDW'''{{jez-eng-lat|enterprise data warehous}}, EDV), issistem aje systemkoji usedse forkoristi za [[Business reporting| reportingizveštavanje]] andi [[data analysis |analizu podataka]] , andi issmatra consideredse asržnom core component ofkomponentom [[ businessBusiness intelligence |poslovne inteligencije]].<ref>{{cite conference |last1=Dedić|first1=Nedim |last2=Stanier|first2=Clare |year=2016 |editor1-last=Hammoudi|editor1-first=Slimane|editor2-last=Maciaszek|editor2-first=Leszek|editor3-last=Missikoff|editor3-first=Michele M. Missikoff|editor4-last=Camp|editor4-first=Olivier|editor5-last=Cordeiro|editor5-first=José|title=An Evaluation of the Challenges of Multilingualism in Data Warehouse Development|url=http://eprints.staffs.ac.uk/2770/|journal=Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)|publisher=SciTePress|volume=1|pages=196–206|conference=International Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy|conferenceurl=https://eprints.staffs.ac.uk/2770/1/ICEIS_2016_Volume_1.pdf|doi=10.5220/0005858401960206|isbn=978-989-758-187-8}}</ref> DWsSkladišta arepodataka centralsu repositoriescentralna ofspremišta integratedintegrisanih datapodataka fromiz onejednog orili moreviše disparaterazličitih sourcesizvora. TheyU storenjima currentse andskladište historicalsadašnji datai inistorijski podaci onena singlejednom placemestu<ref name="rjmetrics">{{cite web|url=https://blog.rjmetrics.com/2014/12/04/10-common-mistakes-when-building-a-data-warehouse/|publisher=blog.rjmetrics.com|title=9 Reasons Data Warehouse Projects Fail|accessdate=2017-04-30}}</ref> thatkoji arese usedkoriste forza creatingizradu analyticalanalitičkih reportsizveštaja forza workersradnike throughoutu theceloj enterprisekompaniji.<ref name="spotlessdata">{{cite web|url=https://web.archive.org/web/20180726071809/https://spotlessdata.com/blog/exploring-data-warehouses-and-data-quality|publisher=spotlessdata.com|title=Exploring Data Warehouses and Data Quality|accessdate=2017-04-30}}</ref>
The data stored in the warehouse is [[upload]]ed from the [[operational system]]s (such as marketing or sales). The data may pass through an [[operational data store]] and may require [[data cleansing]]<ref name="rjmetrics"/> for additional operations to ensure [[data quality]] before it is used in the DW for reporting.
Podaci pohranjeni u skladištu [[Upload|prenose]] se iz [[Operational system|operacionih sistema]] (kao što su marketing ili prodaja). Podaci mogu proći kroz [[Operational data store|operativno skladište podataka]] i mogu zahtevati [[Data cleansing|čišćenje podataka]]<ref name="rjmetrics"/> za dodatne operacije kako bi se osigurao kvalitet podataka pre upotrebe u skladišta podataka za izveštavanje.
The typical [[extract, transform, load]] (ETL)-based data warehouse<ref name="spotlessdata2">{{cite web|url=https://web.archive.org/web/20170217144032/https://spotlessdata.com/what-big-data|publisher=spotlessdata.com|title=What is Big Data?|accessdate=2017-04-30}}</ref> uses [[Staging (data)|staging]], [[data integration]], 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 the disparate data sets by transforming the data from the staging layer often storing this transformed data in an [[operational data store]] (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 facts and aggregate facts. The combination of facts and dimensions is sometimes called a [[star schema]]. The access layer helps users retrieve data.<ref name=IJCA96Patil>{{cite journal |url=http://www.ijcaonline.org/proceedings/icwet/number9/2131-db195 |author1=Patil, Preeti S. |author2=Srikantha Rao |author3=Suryakant B. Patil |title=Optimization of Data Warehousing System: Simplification in Reporting and Analysis |work=IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) |year=2011 |volume=9 |issue=6 |pages=33–37 |publisher=Foundation of Computer Science}}</ref> ▼
▲TheTipično typicalskladište podataka zasnovano na [[extract, transform, load |ekstrakciji, transformaciji, unosu]] ( ETL){{jez- basedeng-lat|extract, datatransform, warehouseload}}, ETL)<ref name="spotlessdata2">{{cite web|url=https://web.archive.org/web/20170217144032/https://spotlessdata.com/what-big-data|publisher=spotlessdata.com|title=What is Big Data?|accessdate=2017-04-30}}</ref> useskoristi [[Staging (data)| stagingpostavljanje]], [[data integration |integraciju podataka]] , andi pristupanje accessslojevima layerskako tobi housese itsomogućile keyključne functionsfunkcije. ThePripremni stagingsloj layerili orscenarijsko stagingskladište databasebaze storespodataka rawsadrži datasirove extractedpodatke fromizvađene eachiz ofsvakog theod disparaterazličitih sourceizvora datapodataka systemsdatog sistema. TheIntegracioni integrationsloj layerintegriše integratesrazličite theskupove disparatepodataka datatransformišući setspodatke byiz transformingscenarijskog thesloja, datačesto fromčuvajući theove stagingtransformisane layerpodatke often storing this transformed data in anu [[operational data store |operativnom skladištu podataka]] ( {{jez-eng-lat|operational data store}}, ODS) database. TheIntegrisani integratedpodaci datase arezatim thenpremeštaju movedu todrugu yetbazu another databasepodataka, oftenkoja calledse thečesto datanaziva warehousei databasebaza podataka skladišta podataka, wheregde thesu data ispodaci arrangedraspoređeni intou hierarchicalhijerarhijske groupsgrupe, oftenčesto calledzvane dimensionsdimenzijama, andu intočinjenice factsi andagregirane aggregate factsčinjenice. TheKombinacija combinationčinjenica ofi factsdimenzija andponekad dimensionsse is sometimes called anaziva [[star schema |shema zvezde]]. ThePristupni accesssloj layerpomaže helpskorisnicima usersda retrievepreuzmu datapodatke.<ref name=IJCA96Patil>{{cite journal |url=http://www.ijcaonline.org/proceedings/icwet/number9/2131-db195 |author1=Patil, Preeti S. |author2=Srikantha Rao |author3=Suryakant B. Patil |title=Optimization of Data Warehousing System: Simplification in Reporting and Analysis |work=IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) |year=2011 |volume=9 |issue=6 |pages=33–37 |publisher=Foundation of Computer Science}}</ref>
The main source of the data is [[data cleansing|cleansed]], transformed, catalogued, and made available for use by managers and other business professionals for [[data mining]], [[OLAP|online analytical processing]], [[market research]] and [[decision support]].<ref>Marakas & O'Brien 2009</ref> However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the [[data dictionary]] are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes [[business intelligence tools]], tools to extract, transform, and load data into the repository, and tools to manage and retrieve [[metadata]].
Glavni izvor podataka se [[data cleansing|čisti]], transformiše, kataloguje i stavlja na raspolaganje za upotrebu menadžerima i drugim poslovnim korisnicima za [[istraživanje podataka]], [[Online analytical processing|onlajn analitičku obradu]], [[market research|istraživanje tržišta]] i [[Sistemi za podršku odlučivanju|podršku pri odlučivanju]].<ref>Marakas & O'Brien 2009</ref> Međutim, sredstva za prikupljanje i analiziranje podataka, izdvajanje, pretvaranje i učitavanje podataka i upravljanje [[data dictionary|rečnikom podataka]] takođe se smatraju bitnim komponentama sistema skladištenja podataka. Mnoge reference o skladištenju podataka koriste ovaj širi kontekst. Stoga, proširena definicija skladištenja podataka obuhvata [[Business intelligence software|alate poslovne inteligencije]], alate za izdvajanje, pretvaranje i učitavanje podataka u skladište i alate za upravljanje i preuzimanje [[Metapodaci|metapodataka]].
== Istorija ==
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