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Infobright Enterprise Edition Analytic Data Warehouse Technology An overview

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Author(s): Juned A. Khan | Ajit P. Shiralkar

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: ncipet;
Issue: 15;
Date: 2012;
Original page

Keywords: Infobright Optimizer | Data Packs and Data Pack Nodes

ABSTRACT
Over the last few decades, business intelligence has emerged as one of the highest priority items on CIO agendas. Businesses and government agencies know that mining information from the increasingly large volumes of data they collect is critical to their business or mission. During this same period, a number of other factors have contributed to the high rate of growth of business intelligence (BI) and data warehousing (DW) technologies including: Many more users with diverse needs Need for ad hoc queries vs. standard canned reports Need for more "real time" information Growth of the number of databases within an organization, with need for consolidation of information. Rapidly growing volumes of data Growth of internet and web-based applications, including selfservice applications Regulatory/legal requirements Traditional approaches to data warehousing have significant drawbacks in terms of effectively delivering a solution to the business for such diverse requirements. These drawbacks include high licensing and storage cost, slow query performance against large data volumes, and difficulty in providing access to all of the data stored in multiple databases. At the same time, these growing issues are placing a high burden on DBAs and IT organizations to implement, tune, and manage databases that supply BI information. The result is that business users are frustrated with how long it takes to get critical analytical information needed for the success of the business. As the need for BI and DW has grown, various new products and technologies have been introduced to address different needs. Many are best suited for workloads that consist of a high volume of planned, repetitive reports and queries. An example of such an application would be a data warehouse used to support a retail call center. Each time a customer calls, the system calls up his or her account. This is a repetitive OLTP-like query that benefits from a specifically designed and engineered system to optimize the performance of these queries. Data warehouses using a traditional index-based architecture are well suited to this workload. But another growing area for data warehousing and BI is analytics. Examples may include, marketing, finance, sales, compliance, risk management, or operations groups performing ad hoc queries such as: "How did a particular 2007 Christmas sales campaign perform compared to our 2006 campaign?" or "Let's analyze why there are more mortgage defaults in this area over the last 12 months versus the last five years." The ad hoc nature and diversity of these requests make row-oriented, indexbased architectures a poor choice for an analytical data warehouse. By definition, DBAs don't know what users will need in the future and are therefore unable to determine what indexes to create. As adding an index adds to both the size of the database and the time needed to load data.
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