{"id":84225,"date":"2020-06-06T07:42:14","date_gmt":"2020-06-06T05:42:14","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/vitriny-dannyh-data-vault"},"modified":"2020-06-06T07:42:14","modified_gmt":"2020-06-06T05:42:14","slug":"vitriny-dannyh-data-vault","status":"publish","type":"post","link":"https:\/\/prohoster.info\/no\/blog\/administrirovanie\/vitriny-dannyh-data-vault","title":{"rendered":"Datavisning DATA VAULT","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>I de tidligere <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/users\/asenterprise\/posts\/\">artikler<\/a><\/noindex>, ble vi kjent med grunnleggende DATA VAULT, utvidelsen av DATA VAULT til en mer analysevennlig tilstand og opprettelsen av BUSINESS DATA VAULT. Det er p\u00e5 tide \u00e5 fullf\u00f8re serien med den tredje artikkelen.<\/p>\n<p>Som jeg annonserte i forrige <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">publikasjonen<\/a><\/noindex>, vil denne artikkelen omhandle emnet BI, mer spesifikt forberedelsen av DATA VAULT som datakilde for BI. La oss se p\u00e5 hvordan vi kan opprette faktabord og dimensjoner og dermed lage et stjerneschema.<\/p>\n<p>Da jeg begynte \u00e5 studere engelskspr\u00e5klige materialer om oppretting av datavarehus over DATA VAULT, fikk jeg f\u00f8lelsen av at prosessen var ganske kompleks. Siden artiklene har et betydelig volum, inneholder de henvisninger til endringer i formuleringene som har oppst\u00e5tt i metodikken Data Vault 2.0, og viktigheten av disse formuleringene blir ogs\u00e5 fremhevet. <\/p>\n<p>Imidlertid, etter \u00e5 ha dykket dypere inn i oversettelsen, ble det klart at prosessen ikke er s\u00e5 vanskelig. Men, kanskje vil du ha en annen mening.<\/p>\n<p>S\u00e5, la oss komme til kjernen.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h3>Dimensjonstabeller og faktabord i DATA VAULT<\/h3>\n<p>\nDen mest vanskelige informasjonen \u00e5 forst\u00e5:<\/p>\n<ul>\n<li>Dimensjonstabeller bygges p\u00e5 informasjonen fra huber og deres satellitter;<\/li>\n<li>Faktabord bygges p\u00e5 informasjon fra linker og deres satellitter.<\/li>\n<\/ul>\n<p>\nOg dette er \u00e5penbart etter \u00e5 ha lest artikkelen om <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">grunnleggende DATA VAULT<\/a><\/noindex>. Hubene lagrer unike n\u00f8kler til forretningsobjekter, deres satellitter er tilstandsattributtene til forretningsobjektene knyttet til tid; satellitter knyttet til linker som st\u00f8tter transaksjoner, lagrer numeriske egenskaper ved disse transaksjonene.<\/p>\n<p>Teorien slutter i prinsippet her.<\/p>\n<p>Men, likevel, etter min mening, er det n\u00f8dvendig \u00e5 merke seg et par begreper som kan dukke opp i artikler om metodikken DATA VAULT:<\/p>\n<ul>\n<li>Raw Data Marts \u2013 vitrinene av \"r\u00e5\" data;<\/li>\n<li>Information Marts \u2013 informasjonsvitriner.<\/li>\n<\/ul>\n<p>\nBegrepet \"Raw Data Marts\" refererer til vitrinene bygget over DATA VAULT-data ved \u00e5 utf\u00f8re relativt enkle JOIN-er. Tiln\u00e6rmingen \"Raw Data Marts\" gj\u00f8r det mulig \u00e5 fleksibelt og raskt utvide prosjektet lagringsmessig med informasjon som er egnet for analyse. Denne tiln\u00e6rmingen krever ikke komplekse datatransformasjoner og utf\u00f8relsen av forretningsregler f\u00f8r dataene legges i vitrinene, men dataene i \"Raw Data Marts\" m\u00e5 v\u00e6re forst\u00e5elige for forretningsbrukeren og tjene som grunnlag for videre transformasjon, for eksempel ved hjelp av BI-verkt\u00f8y.<\/p>\n<p>Begrepet \u201cInformation Marts\u201d oppstod i metodologien Data Vault 2.0, og erstattet det gamle begrepet \u201cData Marts\u201d. Denne endringen er for\u00e5rsaket av en bevissthet om oppgaven \u00e5 implementere datamodellen for rapportering som en transformasjon av data til informasjon. Skjemaet \u201cInformation Marts\u201d skal f\u00f8rst og fremst gi virksomheten beslutningsdyktig informasjon.<\/p>\n<p>Selv om definisjonene er litt omstendelige, gjenspeiler de to enkle fakta:<\/p>\n<ol>\n<li>Vitriner av typen \u201cRaw Data Marts\u201d bygges p\u00e5 r\u00e5 (RAW) DATA VAULT, et lager som kun inneholder grunnleggende konsepter: HUBS, LINKS, SATELLITES;<\/li>\n<li>Vitriner \u201cInformation Marts\u201d bygges med bruk av elementene fra BUSINESS VAULT: PIT, BRIDGE.<\/li>\n<\/ol>\n<p>\nN\u00e5r vi ser p\u00e5 eksempler p\u00e5 lagring av informasjon om ansatte, kan vi si at en vitrine som viser n\u00e5v\u00e6rende (gyldig per i dag) telefonnummer til en ansatt \u2013 dette er en vitrine av typen \u201cRaw Data Marts\u201d. For \u00e5 lage en slik vitrine brukes den forretningsmessige n\u00f8kkelen til den ansatte og funksjonen MAX(), brukt p\u00e5 attributtet for datoen for lasting inn i satellitten (MAX(SatLoadDate)). N\u00e5r det kreves lagring av historien om endringer i attributtet i vitrinene \u2013 m\u00e5 man forst\u00e5 fra hvilken til hvilken dato telefonen var gyldig, den prim\u00e6re n\u00f8kkelen i en slik tabell vil v\u00e6re en kompilasjon av forretningsn\u00f8kkelen og datoen for lasting inn i satellitten, og det legges ogs\u00e5 til et felt for sluttdatoen for gyldighetsperioden.<\/p>\n<p>Opprettelsen av en vitrine som lagrer aktuell informasjon om hvert attributt fra flere satellitter som inng\u00e5r i huben, for eksempel telefonnummer, adresse, navn, inneb\u00e6rer bruk av PIT-tabellen, gjennom hvilken det er enkelt \u00e5 f\u00e5 tilgang til alle gyldighetsdatoer. Vitriner av denne typen tilh\u00f8rer \u201cInformation Marts\u201d.<\/p>\n<p>Begge tiln\u00e6rmingene er aktuelle b\u00e5de for dimensjoner og fakta. <\/p>\n<p>For \u00e5 opprette vitringer som lagrer informasjon om flere linker og huber, kan det brukes tilgang til BRIDGE-tabeller.<\/p>\n<p>Med denne artikkelen avslutter jeg syklusen om konseptet DATA VAULT, og jeg h\u00e5per informasjonen jeg har delt vil v\u00e6re nyttig i implementeringen av deres prosjekter.<\/p>\n<p>Som alltid, til slutt, noen nyttige lenker:<\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.vertabelo.com\/blog\/data-vault-series-building-an-information-mart-with-your-data-vault\/\">Artikkel<\/a><\/noindex> Kenta Grazianos arbeid, som inneholder detaljerte beskrivelser og modeller;<\/li>\n<\/ul>\n<p><\/p>\n<ul>\n<li>Bok: \u201cBuilding a Scalable Data Warehouse with DATA VAULT 2.0\u201d;<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">Artikkel \u201cGrunnleggende om Data Vault\u201d<\/a><\/noindex>;<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">Artikkel \u201cUtviklingen av DATA VAULT og overgangen til BUSINESS DATA VAULT\u201d<\/a><\/noindex>.<\/li>\n<\/ul>\n<p>Kilde: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/505292\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0438\u0445 \u0441\u0442\u0430\u0442\u044c\u044f\u0445, \u043c\u044b \u043f\u043e\u0437\u043d\u0430\u043a\u043e\u043c\u0438\u043b\u0438\u0441\u044c \u0441 \u043e\u0441\u043d\u043e\u0432\u0430\u043c\u0438 DATA VAULT, \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0435\u043c DATA VAULT \u0434\u043e \u0431\u043e\u043b\u0435\u0435 \u043f\u043e\u0434\u0445\u043e\u0434\u044f\u0449\u0435\u0433\u043e \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f \u0438 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u0435\u043c BUSINESS DATA VAULT. \u041d\u0430\u0441\u0442\u0430\u043b\u043e \u0432\u0440\u0435\u043c\u044f \u0437\u0430\u0432\u0435\u0440\u0448\u0430\u0442\u044c \u0441\u0435\u0440\u0438\u044e \u0442\u0440\u0435\u0442\u044c\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435\u0439. \u041a\u0430\u043a \u044f \u0430\u043d\u043e\u043d\u0441\u0438\u0440\u043e\u0432\u0430\u043b \u0432 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u0438, \u044d\u0442\u0430 \u0441\u0442\u0430\u0442\u044c\u044f \u0431\u0443\u0434\u0435\u0442 \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u0430 \u0442\u0435\u043c\u0435 BI, \u0430 \u0442\u043e\u0447\u043d\u0435\u0435 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0435 DATA VAULT \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f BI. \u0420\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-84225","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.5 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0438\u0445 \u0441\u0442\u0430\u0442\u044c\u044f\u0445, \u043c\u044b \u043f\u043e\u0437\u043d\u0430\u043a\u043e\u043c\u0438\u043b\u0438\u0441\u044c \u0441 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