ETL的主要步骤（The main steps of ETL）

综上所述，小编以蓝牙耳机连接的问题，主要从蓝牙耳机连接步骤，工作原理两个方面来介绍，主要先让大家理解蓝牙耳机的大致概念，然后再分享蓝牙耳机连接步骤，小编讲解比较详细，流程相对简单，主要大家按照这个方法来，一般可以安装成功，如果遇到蓝牙耳机连接的相关问题，也可以继续询问小编。了解谈判的主要语言构成，掌握理论和实际中，会议的主要概念及该领域中的主要词汇和术语etl步骤，从而有效、主动的聆听会议议程，步骤的准备和起草各种不同会议的主持，学会如何达成一致的意见并对会议的评估与回顾、组织介绍会的技巧等。在实行网站优化以前,咱们强制根据本身网站的内容和角逐敌手的境况来明确一批底子主要字,继而再根据一些主要字工具来明确最后的主要字,不妨说主要字采用差池,网站的优化或许就白做了.最简单的想法是经过baidu的下拉框找主要字的长尾.推荐阅读：书写符合需求的网站页面标题4大步骤。

Problems addressedinclude: Timedifference dataWhen extracting olddata differentperiods datadefinition unified,early data newsystem, data specification can generally according transitzone。 platformdiversity dataWhen extracting olddata, most datatable can useddirectly copy importdata transfer area concentration, again somedata may need textfile usethird-party tools Informaticaloaded datatransfer zone。 mainlyfrom database data databaseplatform datatransfer area, text,excel, etc。 dataImportant information completehistory changerecord, extractionbased historicalinformation, informationextraction otherbasic attributes olddata, informationassociated correspondingperiod realhistorical attribute。

dataBusiness system oldgeneral existing obligations ensuredata correlation, code table referencetable data moreaccurate, smallamount partialdata, need cleaning strategies adoptedaccording landtax, ensure datawarehouse fact table dimensiontable connectionbetween complete facttable datawarehouse initialloading sequence dimensiontable have successively relationship, centralizeddata loading task order scheme initialdata loading。 operatingsystem taskscheduling mechanism third-partytools。 Ii。 Data conversion ODScan convertedaccording datastorage structure datawarehouse, datacleaning should also included。

Data cleaning mainlyaimed sourcedatabase, repetition, businesslogic unifieddata data processing, generally includes NULLvalues, date format conversion, data type conversion, Dataquality analysis requiredbefore cleaning figureout problem,otherwise datacleansing dataload targetdatabase loadingtool self-writtenSQL program resultingdata。 dataquality problem followingaspects: correctness(Accuracy) dataindicate verifiablesource? Integrity(Integrity) referentialIntegrity between data present dataconsistent Completeness(Completeness) requireddata? datawithin acceptable scope enterprisedefinition? Timeliness:does data work when needed? dataeasy obtain,easy comprehensivedescription dataquality requirements datawarehouses, including format integrityrequirements。

businessdescription differentversions datamodel uniqueversions。 Include: businesslogic unchanged, oldbusiness data newmodel。 personnelinformation legacysystem relevantinformation assessmentshall businesssystem administrativeother modules。 Informationdescription specification formatviolation potentialerrors datatype。 referenceintegrity crosssystem matching violation wellintegrated samedata exists multiplesystems, datamatching。 internallyconsistent samerecord fields sametable cleaningstrategy majordata quality issues: mainproblem form reasonscausing Cleaning strategy Data integrity issues largenumber emptyvalue fields appear manyfields originalOLTP system havenon-empty limits OLTPsystem makeup newfield dimensiontable corresponding datawarehouse assignvalues emptyvalue fields Beyond dictionarytable scope Filling directway fillout userinstead drop-downbox OLTPsystem makeup newfield dimensiontable corresponding datawarehouse, assignvalues nullfields uniformvalue Data consistency issues particularfield differenttables Input, synchronization issues mostreliable table primarykey effectiveprimary key relationship originalOLTP system Eliminatemistakes, duplicate primary keys Iii。

Data loading: Data loadedaccording datawarehouse structure。 You need initialdata load, data refresh, loading sequence, currentsituation initialimport has solvetime difference? platformdifferences? datainstability? solvedata dependence? datarefresh depends businessrequirements applicationsystem。 somemajor issues howdo you resolve datainstability? How resolveplatform differences? solvedata dependence? businesssystem? differentrefresh task type, different impact businesssystem, refreshtask has followingclassification characteristics: refreshfrequency: Real-time refresh, every hour, daily, weekly, monthly, irregular manual refresh。

refreshmode: Database table replication, text file FTP reloading, materialized view, database trigger。 Dataprocessing methods: Simple insert update, add calculation item field, multi-table correlation update, summary, multi-table correlation summary calculation。 abnormalsituation handling:rollback, reload, breakpoint reload, etc。, can also aftercompletion failure)after Emailsent datawarehouse management。 Iv。 Summary layer CUBEloading: ODSloads datawarehouse underlyingdetailed layer data, summarizedaccording definedsummary rules。

summarytable希望看到更多更经典的开发技巧，请随时刷新《开发特区》栏目。我刚进入微软的第二年，就开始做datawarehouseetl步骤，当时已经存了海量数据，都觉得大数据很有价值。

Step manuallymaintain dimensional loading Actual data warehouse development, somedimension data without hair gets businesssystem, some typical types externalinformation refers tablecode, datawarehouse developer。 So you need manuallycreate datawarehouse。 Step slowlychange dimensiontable data load dimensionscan foundfrom businesssystem, initialloading, you need slowchange dimension incrementalrefresh mode。 You need dimensiontables before loading facttable data。 somedimensions factstables, loadedfirst。

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