Data Warehousing and Current Trends

Topics: Data warehouse, Data management, Extract, transform, load Pages: 14 (4292 words) Published: October 28, 2012
Data warehousing and current trends

Submitted to:
Mr. S. Ramanathan


1. Executive Summary
2. Data warehousing basics, difference from database and its business implication 3. Data mining, businesses using it and how
4. ETL technology, businesses using it and how
5. Tools used
6. Data mart and difference in business implication
7. References


This study takes an insight into the usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are: a) Data warehousing

b) Data Mining
c) ETL
d) Data Mart
An attempt has been made to analyse different ways of using these for the enhancement in the different field.

Data warehousing and current trends.
Data Warehouse
A data warehouse is a relational database that is used for reporting and data analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. In other words, the data in a data warehouse is made up of snapshots of a business’s multiple operational data-bases. It comprises of software and hardware optimized for executive information systems (EIS) and decision support systems and is combined to run on-line analytical processing (OLAP), rather than the on-line transaction processing which represents the operations world. The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales etc.), ERP, CRM etc. as shown in the figure below. The data may go through an operational data store for additional operations before they are used in the DW for reporting. In addition to a relational database, a DW environment consists of an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications collectively carry out the process of collating data and delivering it to business users. This process is referred to as Data Warehousing.

A Data warehouse is more than a Database! -
All data warehouses are databases, but all databases are not data warehouses. So how is a data warehouse different from a database? There are a number of fundamental differences which separate a data warehouse from a database – * The key difference between an application database and a data warehouse is that while the former is used to record, the latter is designed to carry out analysis and provide answers to questions that are important for your organisation.  * Another difference between the two is that most databases are single application based and only transaction based. The data is analysed within a single domain, but at times multiple domains are also used. * Some of the separate units that may be included within a database are payroll or inventory. Each system deals with one subject, and it will not deal with other areas. Whereas data warehouses deal with multiple domains simultaneously. * The data warehouse finds connections between the multiple subject areas while dealing with them. Through this the data warehouse can show how the company is performing as a whole, rather than in individual areas in case of database. * Another important aspect of data warehouses is their ability to carry out the analysis of trends. They are not volatile, and the data stored in them doesn't change as much as it would in a common database. * Data warehouses also typically keep a very long history from several years to the entire life of the company so that long term trends can be viewed and analysed. * A normal database is used for...
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