DATA WAREHOUSING

DATA WAREHOUSING

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  Group Members RAKSHA JAIN[BBA015011] SEJAL GAIKWAD [BBA015052] AMITA PATIL [BBA015055] TRUPTI JUMLE [BBA015057] Submitted To: Prof.Shalaka DATA WAREHOUSE
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  WHAT IS ADATA WAREHOUSE?WHAT IS A DATA WAREHOUSE? A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context.
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  DATA WAREHOUSINGDATA WAREHOUSING
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  CHARACTERISTICS OFCHARACTERISTICS OF DATAWAREHOUSINGDATA WAREHOUSING  Subject oriented: Gives information about a particular subject  Integrated: Combination of data from multiple and varied resources into one database  Time-variant: All the data in data warehouse is identified with a particular time period.  Non-volatile: Data is stable in data warehouse. Data can be added but is never removed. The final result is homogenous data, which can beThe final result is homogenous data, which can be more easily manipulated.more easily manipulated.
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  HISTORYHISTORY The concept ofdata warehouse dates back to 1980’s whenThe concept of data warehouse dates back to 1980’s when IBMIBM researchersresearchers BARY DEVLINBARY DEVLIN andand PAUL MURPHYPAUL MURPHY developeddeveloped “the business data warehouse”.“the business data warehouse”. 1970’s- ACNielsen and IRI provide dimensional1970’s- ACNielsen and IRI provide dimensional data marts for retail sales.data marts for retail sales. 1983- Tera data introduces a database management system1983- Tera data introduces a database management system specifically designed for decision support.specifically designed for decision support.
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  ADVANTAGESADVANTAGES  It providesbusiness users with a customer-centric view of company’s data which helps to integrate data from sale, service, manufacturing and distribution and other customer related business systems.  It consolidates data about individual customers and provides a repository of all customer contacts for customer retention planning and cross sales analysis.  It reports on trends across multidivisional, multinational operating units including trends or relationships in areas such as merchandising , production planning , etc
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  DISADVANTANGESDISADVANTANGES  Data warehousesare not optimal for unstructured data.  Because data must be extracted, transformed and loaded into warehouse, there is an element of latency in data warehouse.  Its maintenance cost is high.  Data warehouse can get outdated relatively quickly.  There is often a fine line between data warehouse and operational systems . Delicacy, expensive functionality may be developed .
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  DATA WAREHOUSE ARCHITECTUREDATAWAREHOUSE ARCHITECTURE  AT THE TOP – AAT THE TOP – A CENTRALIZED DATABASECENTRALIZED DATABASE Generally Configured For Queries And Appends – Not Transactions Many Indices, Materialized Views, Etc. Data is loaded and periodically updated via Extract/Transform/Load (ETL) tools Data Warehouse ETL ETL ETL ETL RDBMS1 RDBMS2 HTML1 XML1 ETL pipeline outputs ETL
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  12 Rules OfA Data Warehouse12 Rules Of A Data Warehouse Data Warehouse and Operational Environments are Separated Data is integrated Contains historical data over a long period of time Data is a snapshot data captured at a given point in time Data is subject-oriented Development Life Cycle has a data driven approach versus the traditional process-driven approach Contains a chargeback mechanism for resource usage that enforces optimal use of data by end users Mainly read-only with periodic batch updates
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  CONT..CONT.. Data contains several levelsof detail  Current, Old, Lightly Summarized, Highly Summarized Environment is characterized by Read- only transactions to very large data sets Metadata is a critical component  Source, Transformation, Integration, Storage, Relationships, History, Etc. System that traces data sources, transformations, and storage
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  DATA WAREHOUSE FORDATAWAREHOUSE FOR DECISION SUPPORTDECISION SUPPORT
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  BUSINESS INTELLIGENCE AND DATAWAREHOUSING
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  Thank YouThank You