{"id":84189,"date":"2020-06-05T19:42:34","date_gmt":"2020-06-05T17:42:34","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/razvitie-data-vault-i-perehod-k-business-data-vault"},"modified":"2020-06-05T19:42:34","modified_gmt":"2020-06-05T17:42:34","slug":"razvitie-data-vault-i-perehod-k-business-data-vault","status":"publish","type":"post","link":"https:\/\/prohoster.info\/da\/blog\/administrirovanie\/razvitie-data-vault-i-perehod-k-business-data-vault","title":{"rendered":"Udviklingen af DATA VAULT og overgangen til BUSINESS DATA VAULT","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>I den tidligere artikel har jeg beskrevet grundlaget for DATA VAULT, forklaret de vigtigste elementer i DATA VAULT og deres form\u00e5l. Emnet DATA VAULT kan dog ikke betragtes som udt\u00f8mt, da vi skal tale om de n\u00e6ste skridt i evolutionen af DATA VAULT.<\/p>\n<p>I denne artikel vil jeg fokusere p\u00e5 udviklingen af DATA VAULT og overgangen til BUSINESS DATA VAULT eller blot BUSINESS VAULT.<\/p>\n<h3>\u00c5rsagerne til opkomsten af BUSINESS DATA VAULT<\/h3>\n<p>\nDet b\u00f8r bem\u00e6rkes, at DATA VAULT, selvom det har visse styrker, ikke er uden svagheder. En af disse svagheder er kompleksiteten ved at skrive analytiske foresp\u00f8rgsler. Foresp\u00f8rgslerne har et betydeligt antal JOINs, og koden bliver lang og klodset. Derudover gennemg\u00e5r dataene, der kommer ind i DATA VAULT, ingen transformationer, hvilket betyder, at DATA VAULT i sin rene form ikke n\u00f8dvendigvis har v\u00e6rdi for forretningen.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nNetop for at afhj\u00e6lpe disse svagheder er metodologien DATA VAULT blevet udvidet med elementer som:<\/p>\n<ul>\n<li>PIT (point in time) tabeller;<\/li>\n<li>BRIDGE tabeller;<\/li>\n<li>PREDEFINED DERIVATIONS.<\/li>\n<\/ul>\n<p>\nLad os se n\u00e6rmere p\u00e5 form\u00e5let med disse elementer.<\/p>\n<h3>PIT tabeller<\/h3>\n<p>\nGenerelt kan \u00e9n forretningsenhed (HUB) have data med forskellige opdateringsfrekvenser. For eksempel, n\u00e5r vi taler om data, der karakteriserer en person, kan vi sige, at oplysninger om telefonnummer, adresse eller e-mail har en h\u00f8jere opdateringsfrekvens end for eksempel navn, pasoplysninger, civilstand eller k\u00f8n.<\/p>\n<p>Derfor b\u00f8r man tage h\u00f8jde for opdateringsfrekvensen, n\u00e5r man definerer satellitter. Hvorfor er det vigtigt? <\/p>\n<p>Hvis man opbevarer attributter med forskellige opdateringsfrekvenser i \u00e9n tabel, skal man tilf\u00f8je en r\u00e6kke i tabellen ved hver opdatering af den mest hyppigt \u00e6ndrede attribut. Som f\u00f8lge heraf \u00f8ges diskpladsen, og udf\u00f8relsestiden for foresp\u00f8rgslerne stiger.<\/p>\n<p>Nu, n\u00e5r vi har opdelt satellitterne efter opdateringsfrekvens, og vi kan indl\u00e6se data i dem uafh\u00e6ngigt, skal vi sikre os mulighed for at f\u00e5 adgang til aktuelle data. Helst uden at bruge un\u00f8dvendige JOINs.<\/p>\n<p>For example, it is necessary to obtain up-to-date information from satellites with different update frequencies. This not only requires making a JOIN but also creating several nested queries (to each satellite containing information) with the selection of the maximum update date MAX(Update Date). With each new JOIN, such code grows and quickly becomes complex to understand.<\/p>\n<p>The PIT table is designed to simplify such queries, and the PIT tables are filled simultaneously with the recording of new data into the DATA VAULT. PIT table:<\/p>\n<p><img decoding=\"async\" alt=\"Udviklingen af DATA VAULT og overgangen til BUSINESS DATA VAULT\" src=\"\/wp-content\/uploads\/2020\/06\/f3e0c2068e075cd5fba5321c47d755fa.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nThus, we have information about the relevance of data across all satellites at any given moment. By using JOINs to the PIT table, we can completely eliminate nested queries, provided that the PIT is filled daily and without omissions. Even if there are gaps in the PIT, the latest data can only be obtained using a single nested query to the PIT itself. One nested query will run faster than nested queries to each satellite.<\/p>\n<h3>BRIDGE<\/h3>\n<p>\nBRIDGE-type tables are also used to simplify analytical queries. However, unlike the PIT, they serve to simplify and accelerate queries between different hubs, links, and their satellites.<\/p>\n<p>The table contains all necessary keys for all satellites that are frequently used in queries. Additionally, if needed, hashed business keys can be supplemented with textual keys when the names of keys are required for analysis.<\/p>\n<p>The fact is that without the use of BRIDGE, obtaining data from satellites belonging to different hubs will require not only joining the satellites themselves but also the links that connect the hubs.<\/p>\n<p>The presence or absence of a BRIDGE is determined by the storage configuration and the need to optimize the speed of query execution. It is difficult to come up with a universal example of a BRIDGE.<\/p>\n<h3>PREDEFINED DERIVATIONS<\/h3>\n<p>\nAnother type of object that brings us closer to the BUSINESS DATA VAULT is tables containing pre-calculated metrics. Such tables are indeed important for business, as they contain information aggregated according to specified rules and allow relatively easy access to this information.<\/p>\n<p>Arkitektoniske PREDEFINED DERIVATIONS repr\u00e6senterer intet mindre end endnu en satellit af et bestemt hub. Den, ligesom en almindelig satellit, indeholder forretningsn\u00f8gle og dato for oprettelse af indtastningen i satellitten. Her oph\u00f8rer dog lighederne. Den videre sammens\u00e6tning af attributter for en s\u00e5dan \"specialiseret\" satellit bestemmes af forretningsbrugere baseret p\u00e5 de mest efterspurgte, forudberegnede indikatorer.<\/p>\n<p>For eksempel kan en hub, der indeholder information om en medarbejder, inkludere en satellit med s\u00e5danne indikatorer som:<\/p>\n<ul>\n<li>Minimumsl\u00f8n;<\/li>\n<li>Maksimall\u00f8n;<\/li>\n<li>Gennemsnitsl\u00f8n;<\/li>\n<li>Akkumuleret resultat af udbetalt l\u00f8n osv.<\/li>\n<\/ul>\n<p>\nDet er logisk at inkludere PREDEFINED DERIVATIONS i PIT-tabellen for denne samme hub, s\u00e5 man nemt kan f\u00e5 datask\u00e6ringer for medarbejderen p\u00e5 en specifikt valgt dato.<\/p>\n<h3>KONKLUSIONER<\/h3>\n<p>\nSom praksis viser, er brugen af DATA VAULT for forretningsbrugere en smule besv\u00e6rlig af flere grunde:<\/p>\n<ul>\n<li>Koden til foresp\u00f8rgsler er kompleks og volumin\u00f8s;<\/li>\n<li>Overfloden af JOINs p\u00e5virker foresp\u00f8rgslernes hastighed;<\/li>\n<li>For at skrive analytiske foresp\u00f8rgsler kr\u00e6ves en fremragende viden om struktur af lageret.<\/li>\n<\/ul>\n<p>\nFor at forenkle adgangen til data udvides DATA VAULT med yderligere objekter:<\/p>\n<ul>\n<li>PIT (point in time) tabeller;<\/li>\n<li>BRIDGE tabeller;<\/li>\n<li>PREDEFINED DERIVATIONS.<\/li>\n<\/ul>\n<p>\nI n\u00e6ste <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/505292\/\">artikel<\/a><\/noindex> jeg planl\u00e6gger at dele, hvad jeg synes er det mest interessante, for dem der arbejder med BI. Jeg vil pr\u00e6sentere m\u00e5der at oprette faktatabeller og dimensionstabeller p\u00e5 baggrund af DATA VAULT.<\/p>\n<p>Materialerne i artiklen er baseret p\u00e5:<\/p>\n<ul>\n<li>P\u00e5 <noindex><a rel=\"nofollow\" href=\"https:\/\/www.vertabelo.com\/blog\/data-vault-series-the-business-data-vault\/\">publikation<\/a><\/noindex> af Kenta Graziano, som udover en detaljeret beskrivelse indeholder modelleringsskemaer;<\/li>\n<li>Bogen: \"Building a Scalable Data Warehouse with DATA VAULT 2.0\";<\/li>\n<li>Artikel <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/502968\/\">Grundl\u00e6ggende i Data Vault<\/a><\/noindex>.<\/li>\n<\/ul>\n<p>Kilde: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/504214\/\">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\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u044f \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u043b \u043e\u0431 \u043e\u0441\u043d\u043e\u0432\u0430\u0445 DATA VAULT, \u043e\u043f\u0438\u0441\u0430\u043b \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0435 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u044b DATA VAULT \u0438 \u0438\u0445 \u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435. \u041d\u0430 \u044d\u0442\u043e\u043c \u043d\u0435\u043b\u044c\u0437\u044f \u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0442\u0435\u043c\u0443 DATA VAULT \u0438\u0441\u0447\u0435\u0440\u043f\u0430\u043d\u043d\u043e\u0439, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c \u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0441\u0442\u0443\u043f\u0435\u043d\u044f\u0445 \u044d\u0432\u043e\u043b\u044e\u0446\u0438\u0438 DATA VAULT. \u0418 \u0432 \u044d\u0442\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u044f \u0441\u043a\u043e\u043d\u0446\u0435\u043d\u0442\u0440\u0438\u0440\u0443\u044e\u0441\u044c \u043d\u0430 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0438 DATA VAULT \u0438 \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u0443 \u043a BUSINESS DATA VAULT \u0438\u043b\u0438 \u043f\u0440\u043e\u0441\u0442\u043e BUSINESS VAULT. \u041f\u0440\u0438\u0447\u0438\u043d\u044b \u043f\u043e\u044f\u0432\u043b\u0435\u043d\u0438\u044f [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":84190,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-84189","post","type-post","status-publish","format-standard","has-post-thumbnail","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 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