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DataWarehousing Fundamentals

20 September, 2018

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    1     ETL Tool E – Extract 2setting the data T-transform2doing Intermediate operation L-Load2Load to destination   Tools Data stage, Informatica, AB-Initio, SSIS, OWB et   Analytical tool To create multi – Dimensional object and object and provides supports for multi –Dimensional analysis   Tools  COGNOS, BO, Hyperion, SSAS, Micro strategy, OBIEE etc.   Reporting tools To display the data in the understand and required format, we go for this,   Tools COGNOS, BO, SSRS, Hyperion, Micro strategy B Crstal on  OBIEE etc.  
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It is composed on observable and recordable facts, that are off be found in transaction(or)operation system   Information  It is an integrated collection of facts and  is used as the[organized – data is called information] bases for decision making   Need of dataware housing To have single version of data, in single storage To complete with other organization market To analysis tactical strategic queries etc. Definition of ODS, DWH, BI, OLTP, OLAP QLTP  On line transaction processing Here day to day operations data available (immediate operation data) Here all the DML operation can be performed. ODS(Operational data store) It is data stag structure that acts like repository for near real time operational data rather than long trand data. This ODS may for them become to enterprise operational database(or) stage area to data were house DWH(Data were house) It is data base structure, where huge data store, so that we can take appropriate decision OLAP(Online Analytical processing) It is an application that collects managing and present multi– dimensional data analysis and management purpose. BI(Business Intelligence) BI retrieve the data from data warehouse to make business decision and management(it can takes the  help OLAP) Data warehouse Definition There are many authors founder define different approaches INMON SEAM KELLY etc.   INMON Some people calling INMON father of data ware house A Data ware house is subject oriented Integrated. Time variant Non-volatile, Data Granularity Collection or data, in this support of Decision making SEAM KELLY A Data warehouse is subject oriented, integrated, Non- volatile, time variant, separate collection of data   Subject Oriented The data in data ware house should belong to a specific subject Area, Domain (or) Business   Example Entire banking, Information in single place Entire medias information in single place   Integrated By loading the data we need follows standardized mechanism, we have single management data   Example Naming conversion, coding measurement, data attribute etc.   Non – volatile Data in the data were house data, should not updata and delete.   Time variant The data should be store based on time frame so that, we cab current month sales, what is   Previous month sales, what could be sales of future month
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Separate  Where  have should be separate traditional system   Accessible Where have should be high available etc.  

Data Warehousing life cycle

It is combination of 3 life cycles creating a data base and modeling database. ETL requirement gathering ETL Analysis ETL Design ETL Coding ETL Testing ETL deployment and maintenance Analytical life cycle Requirement gathering analysis Designing the objects Create multi-dimensional object Testing multi- dimensional object Deployment implement Reporting life cycle Reporting Testing Reporting Design Reporting Coding Reporting Publishers And maintenance.
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