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Title: SQL Server-Database Design and SSIS
Description: SQL Server-Database Design and SSIS

Introduction

Microsoft SQL Server 2008 represents an excellent choice for the construction and maintenance of data warehouses in enterprises of all sizes.

The term Business Intelligence (BI) describes the process of extracting information from data. The operational data in most enterprises is held in transaction-based systems with specific functions (HR, Sales, Finance, and so on). Frequently the information requested by decision makers within the enterprise requires data from several of the operational systems. Indeed, the more general the question, such as What is our current profit? the more operational systems are likely to be involved in providing data.

An integral part of any BI system is the data warehousea central repository of data that is regularly refreshed from the source systems. The new data is transferred at regular intervals (often nightly) by extract, transform, and load (ETL) processes.

Typically the data in the data warehouse is structured as a star schema [Kim08] although it may also be structured as normalized relational data [Inmon05] or as a hybrid between the two. No matter which structure is chosen, after the new data has been loaded into the data warehouse,

Data warehouses have been built in one form or another for over 20 years. Early in their history it became apparent that building a successful data warehouse is not a trivial undertaking. The IDC report from 1996 [IDC96] is a classic study of the state of data warehousing at the time. Paradoxically, it was used by both supporters and detractors of data warehousing.

The supporters claimed that it proved how effective data warehousing is, citing that for the 62 projects studied, the mean return on investment (ROI) over three years was just over 400 percent. Fifteen of those projects (25 percent) showed a ROI of more than 600 percent.

The detractors maintained that the report was a searing indictment of current data warehouse practices because of 45 projects (with outliers discounted) 34 percent failed to return even the cost of investment after five years. A warehouse that has shown no return in that length of time is not a good investment.

Both sets of figures are accurate and, taken together; reflect the overall findings of the paper itself which says One of the more interesting stories is found in the range of results. While the 45 organizations included in the summary analysis reported ROI results between 3% and 1,838%, the total range varied from as low as 1,857% to as high as 16,000%!

Worryingly, the trend towards failure for data warehouse projects continues today: some data warehouses show a huge ROI, others clearly fail. In a report some nine years later (2005), Gartner predicted that More than 50 percent of data warehouse projects will have limited acceptance or will be failures through 2007.

Does this mean that we have learned nothing in the intervening time? No, we have learned a great deal about how to create successful data warehouses and a set of best practices has evolved. The problem seems to be that not everyone in the field is aware of those practices.

In this paper we cover some of the most important data warehousing features in SQL Server 2008 and outline best practices for using them effectively. In addition, we cover some of the more general best practices for creating a successful data warehouse project. Following best practices alone cannot, of course, guarantee that your data warehouse project will succeed; but it will improve its chances immeasurably. And it is undeniably true that applying best practices is the most cost-effective investment you can make in a data warehouse.

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