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DATA WAREHOUSE AND ITS APLICATIONS IN AGRICULTURE K.P.Wagh Dr. Satish R. In other words Data warehouse is a database that is used to hold data for reporting and analysis. Introduction A Data warehouse [1] is a repository of integrated information, available for queries and analysis. In other words Data warehouse is a database that is used to hold data for reporting and analysis. Goals of Data Warehousing Data Warehouse Architecture Operational Source Systems Operational source systems [1] are developed to capture and proces original busines transactions. These systems are designed for data entry, not for reporting, but it is from here the data in data warehouse gets populated. Data Staging Area Data staging area is where the raw operational data is extracted, cleaned, transformed and combined so that it can be reported on and queried by users. The extract step is the first step of geting data into the data warehouse environment. Extracting means reading and understanding the source data, and copying the pas that are neded to the data staging for further work. Once the data is extracted into the data staging area, there are many transformation steps, including 1. Cleaning the data by corecting mispelings, resolving domain conflicts, dealing with mising data elements, and parsing into standard formats. Purging selected fields from the legacy data that are not useful for data warehouse. Loading in the data warehouse environment usualy takes the form of replicating the dimensional tables and fact tables and presenting these tables to bulk loading facilitates each recipient data mart. The target data mart must then index the newly arived data for query performance. Data Mart Data mart is a logical subset of an enterprise-wide data warehouse. For example, a data warehouse for a retail chain is constructed incrementaly from individual, conformed data marts dealing with separate subject areas such as product sales. Dimensional data marts are organized by subject area such as sales, finance, and marketing and cordinated by data category such as customer, product, and location. Data Warehouse Database A data warehouse database contains the data that is organized and stored specificaly for direct user queries and reports. Metadata Metadata defines the content and location of the data in the data warehouse, relationships betwen the operational databases and the data warehouse and the busines views of the data in the data in the warehouse as acesible to the end-user tols. Thus, any data warehouse design should asure that there is a mechanism that populates and maintains the metadata repository and that al aces paths to data warehouse have metadata as an entry point. Meta data definition can be done by the user in any given data warehousing environment. Warehouse Schema Design gained popularity and aceptance for data warehouse implementation. Crucial Decision in Designing a Data Warehouse The job of designing and implementing a data warehouse [3] is very chalenging and dificult one, even though at the same time, there is a lot of focus and importance atached to it. The designer of the data warehouse may be asked by the top management: take al enterprise data and build a data warehouse such that the management can get answer to al their questions . The recent trend is to build data marts for before a real large data warehouse is built. Al the above steps are required before the data warehousing is implemented. The final step or step 10 is implemented a simple data warehouse or data mart. A clear picture emerges from the entire project on the data warehousing as to what are their problems and how they can be posibly solved with the help of data warehousing. The software tols for building, operating and using Data Warehouse Hardware Platform Organization normaly tend to utilize the already existing hardware platform for data warehouse development however the disk storage requirements for a data warehouse wil be significantly large, especialy in comparison with single aplication. If data warehouse or data mart is smal in data size, normal Pentium server wil be probably suficient with not very high reliability standards. However for a regular large data warehouse aplication the server has to be specialized for the tasks asociated with a data warehouse. Therefore, the requirement of data warehouse server is the scalable high performance for data loading and ad hoc query procesing as wel as the ability to suport large database in a reliable and eficient maner. In web enabled data warehouses, isues of security privacy and acesibility ned to be considered carefuly .Acordingly web enablement facilities should be ensured in the software tols used for data warehouse development. Folowing are the steps of the Data Warehouse implementation: Step 1: Colect and analyze busines requirement. Step 4: Chose the DBMS and software platform for data warehouse. Step 5: Extract the data from operational data sources, translate it, clean-up and load into the data warehouse model or data mart. Aces Tols With the exception of SAS of SAS institute , al the Data Warehouses /OLAP vendors are not curently providing comprehensive single-window software tols capable of handling al aspects of data warehousing project implementation .SAS alone mets the requirement largely independently as it has its own database internaly with a capability of import data from any vendor DBMS software. Therefore one can implement a data warehousing and data mining solution independently with SAS. Artificial inteligence techniques for hypothesis testing, trends discovery, identification and validation of data clusters and segments also useful for data mining 6. A number of query tols are available in the market today which enables an ordinary user to build customized reports by easily composing and executing ad hoc queries without any necesity to have the knowledge of the underlying design details or data base technology, SQL, or even the data model 5. Conclusions Analytical exploration of vast amount of agricultural data can best be suport by apropriate aplication of Data Warehousing and OLAP technologies. A Data Warehouse provides eficient and reliable structure of storage for vast amount data while OLAP techniques provide mechanisms for analysis of this data. gis web services gis web services in Hermosa beach

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