{"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\/da\/blog\/administrirovanie\/vitriny-dannyh-data-vault","title":{"rendered":"Data Vault Vitrine","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>I de foreg\u00e5ende <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/users\/asenterprise\/posts\/\">artikler<\/a><\/noindex>, l\u00e6rte vi om grundl\u00e6ggende DATA VAULT, udvidelsen af DATA VAULT til en mere analyserbar tilstand og oprettelsen af BUSINESS DATA VAULT. Det er tid til at afslutte serien med den tredje artikel.<\/p>\n<p>Som jeg annoncerede i den tidligere <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">publikation<\/a><\/noindex>, vil denne artikel fokusere p\u00e5 emnet BI, n\u00e6rmere bestemt forberedelse af DATA VAULT som datakilde til BI. Lad os se p\u00e5, hvordan man opretter faktatabeller og dimensioner, og dermed skabe en stjerneschema.<\/p>\n<p>Da jeg begyndte at studere engelsksprogede materialer om oprettelse af datavarehuse over DATA VAULT, fik jeg indtryk af, at processen var rimelig kompleks. Artiklerne er omfattende, og der henvises til \u00e6ndringer i formuleringer, der er opst\u00e5et i Data Vault 2.0-metodologien, og betydningen af disse formuleringer understreges. <\/p>\n<p>Men efter at have dykket ned i overs\u00e6ttelsen, blev det klart, at denne proces ikke er s\u00e5 kompliceret. Men m\u00e5ske vil du have en anden mening.<\/p>\n<p>S\u00e5 lad os komme til sagen.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h3>Dimensionstabeller og faktatabeller i DATA VAULT<\/h3>\n<p>\nDen mest udfordrende information at forst\u00e5:<\/p>\n<ul>\n<li>Dimensionstabeller bygges p\u00e5 information fra hubs og deres satellitter;<\/li>\n<li>Faktatabeller bygges p\u00e5 information fra links og deres satellitter.<\/li>\n<\/ul>\n<p>\nOg det er \u00e5benlyst efter at have l\u00e6st artiklen om <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">grundl\u00e6ggende DATA VAULT<\/a><\/noindex>. Hubs gemmer unikke n\u00f8gler til forretningsobjekter, deres satellitter lagrer tilstandsattributter til forretningsobjekter knyttet til tid, satellitter knyttet til links, der underst\u00f8tter transaktioner, gemmer numeriske egenskaber ved disse transaktioner.<\/p>\n<p>P\u00e5 dette punkt afsluttes teorien.<\/p>\n<p>Men alligevel er det for mig vigtigt at bem\u00e6rke et par begreber, som du kan st\u00f8de p\u00e5 i artikler om DATA VAULT-metodologien:<\/p>\n<ul>\n<li>Raw Data Marts \u2013 vitriiner af \"r\u00e5\" data;<\/li>\n<li>Information Marts \u2013 informationsvitriiner.<\/li>\n<\/ul>\n<p>\nBegrebet \"Raw Data Marts\" refererer til vitriiner bygget over DATA VAULT-data ved at udf\u00f8re ret enkle JOINs. Tilgangen til \"Raw Data Marts\" muligg\u00f8r en fleksibel og hurtig udvidelse af projektlageret med information, der er velegnet til analyse. Denne tilgang indeb\u00e6rer ikke komplekse datatransformationer eller implementering af forretningsregler f\u00f8r data placeres i vitriinen, men \"Raw Data Marts\" skal v\u00e6re forst\u00e5elige for forretningsbrugeren og tjene som grundlag for videre omformulering, for eksempel ved brug af BI-v\u00e6rkt\u00f8jer.<\/p>\n<p>Begrebet \u201cInformation Marts\u201d opstod i Data Vault 2.0 metodologien, og det erstattede det gamle begreb \u201cData Marts\u201d. Denne \u00e6ndring skyldes anerkendelsen af opgaven med at implementere datamodellen for rapportering som en transformation af data til information. Schemaet \u201cInformation Marts\u201d skal f\u00f8rst og fremmest sikre, at virksomheden f\u00e5r beslutningsrelevant information.<\/p>\n<p>De langtg\u00e5ende definitioner afspejler to enkle fakta:<\/p>\n<ol>\n<li>Vitriner af typen \u201cRaw Data Marts\u201d er bygget p\u00e5 det r\u00e5 (RAW) DATA VAULT, et lager der kun indeholder grundl\u00e6ggende begreber: HUBS, LINKS, SATELLITES;<\/li>\n<li>Vitriner \u201cInformation Marts\u201d bygger p\u00e5 elementerne i BUSINESS VAULT: PIT, BRIDGE.<\/li>\n<\/ol>\n<p>\nHvis vi ser p\u00e5 eksemplerne p\u00e5 opbevaring af information om medarbejdere, kan vi sige, at en vitrine, der viser det aktuelle (g\u00e6ldende i dag) telefonnummer for medarbejderen \u2014 er en vitrine af typen \u201cRaw Data Marts\u201d. For at danne en s\u00e5dan vitrine bruges medarbejderens forretningsn\u00f8gle og funktionen MAX(), der anvendes p\u00e5 attributten for indl\u00e6sningsdatoen i satellitten (MAX(SatLoadDate)). N\u00e5r det er n\u00f8dvendigt at opbevare historikken for \u00e6ndringer af attributten i vitringen \u2013 er det vigtigt at forst\u00e5, fra hvilken dato til hvilken dato telefonen var aktuel; den prim\u00e6re n\u00f8gle i en s\u00e5dan tabel vil v\u00e6re en kompilation af forretningsn\u00f8glen og indl\u00e6sningsdatoen i satellitten, og der tilf\u00f8jes ogs\u00e5 et felt for slutdatoen for relevansperioden.<\/p>\n<p>Oprettelse af en vitrine, der opbevarer den aktuelle information om hver attribut fra flere satellitter i hubben, s\u00e5som telefonnummer, adresse, fulde navn, indeb\u00e6rer brug af PIT-tabellen, hvori det er let at f\u00e5 alle aktualitetsdatoer. Vitriner af denne type betragtes som \u201cInformation Marts\u201d.<\/p>\n<p>Begge tilgange er relevante b\u00e5de for dimensioner og fakta. <\/p>\n<p>For at oprette vitriner, der opbevarer information om flere links og hubs, kan brugen af BRIDGE-tabeller v\u00e6re n\u00f8dvendig.<\/p>\n<p>Med denne artikel afslutter jeg min serie om DATA VAULT-konceptet, og jeg h\u00e5ber, at den information, jeg har delt, vil v\u00e6re nyttig i implementeringen af dine projekter.<\/p>\n<p>Som altid, til slut, et par nyttige links:<\/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\/\">Artikel<\/a><\/noindex> af Kenta Graziano, som udover en detaljeret beskrivelse indeholder modelleringsskemaer;<\/li>\n<\/ul>\n<p><\/p>\n<ul>\n<li>Bog: \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\/\">Artikel \u201cGrundlaget for Data Vault\u201d<\/a><\/noindex>;<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">Artikel \u201cUdvikling af 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 5.0.3 - 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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