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Data Warehouse options for Analytics

By: Philip Howard
Format: White Paper
Published: November 2007

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Increased competition and a fast moving business environment means that there is a much greater demand for analytics and business intelligence than ever before, to better understand customers for example. This urgency is frequently combined with a desire not just to analyse the past but also to help to predict the future: for instance, how a customer is likely to react to a special offer. Moreover, companies increasingly want to do this sort of analysis in as close to real- time as possible, because environments like call centres require information that is both up-to-date and immediate. Moreover, the audience scope for analytics has also greatly expanded. Historically, business intelligence was limited to a few expert business analysts and the production of standard reports. Today, however, you may want to provide wider access to a broad business or customer community for ad hoc queries, to embed queries into business processes or operational environments such as call centres, or present information within a real- time dashboard.

All of this change is further exacerbated by the growth in the amount of data that needs to be analysed to meet these needs. Many data warehouses, especially those supporting critical business processes, must now expect to have to incorporate tens or even hundreds of terabytes of raw data. Further, the variety of data that needs to be analyzed is also evolving, producing a need for techniques to query non- traditional types of data such as text, video images, geospatial data and so forth.

Given this hugely more complex landscape for queries and analytics it is perhaps no surprise that in recent years the data warehousing market (which serves these business intelligence needs) has itself become more complicated. Organisations now have a number of new data warehousing options offering a variety of approaches that have changed the data warehousing landscape.

It should be clear that different organisations will have different requirements when it comes to business intelligence and analytics. However, the difficulty that arises is the question of how to match that set of requirements against the various data warehouse technologies and products that are available. Each of these options is better suited to some environments than others.

In this paper we will start by considering some of the most important generic issues that need to be considered when selecting the provider of a data warehouse. We will then consider these data warehouse options in the light of some specific business applications for analytics, including on-demand analytics and advanced analytics. While these analytic uses reflect specific applications common to customers of Sybase IQ analytics server, Sybase’s data warehouse product, we will consider the relative merits of various vendors’ technologies or types of technology, throughout the discussions that follow. Vendors that will be considered include Sybase IQ, a column-based analytics server; traditional row-based databases from Oracle, Microsoft SQL Server, IBM DB2 and MySQL; Teradata; and the new data warehouse appliances from Netezza, Hewlett-Packard, Dataupia and DATAllegro.