What is Real Time Data Warehousing?

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  • Written By: Malcolm Tatum
  • Edited By: Bronwyn Harris
  • Last Modified Date: 05 November 2019
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Also known as active data warehousing, real time data warehousing is the process of storing and analyzing data in some type of storage system. Companies tend to make use of this approach in an ongoing effort to maximize the usefulness of various forms of business intelligence, especially in terms of positioning the company for growth through sales. By capturing the information at it becomes available and assimilating the data with historical information, it is possible to predict shifts in customer demand, as well as develop new marketing strategies that will draw in new customers.

The basic process of real time data warehousing requires that data added to a transactional database, such as an order placement or invoicing system, is immediately analyzed, classified, and related to information that is already warehoused from previous transactions. Ideally, the additional information will yield additional information that helps to point to trends within the purchase of goods or services offered by the company, the generation of profits or losses.


By assimilating and assessing transactions and other data as they occur, the company can move faster to take advantage of an upward trend that holds the potential to earn the business a significant return. Because the process of real time data warehousing is automatic, there is no need for anyone to activate this trickling down of data from various transactional databases into the central real time database. Thus, it is possible to access the updated bank of information at any time, and use that data to plan future projects or actions that will be in the best interests of the company.

This process of data mining in a real time fashion can also include automatic generation of reports that are customized to the needs of the end user. The data warehouse design often allows users to choose from a set of pre-programmed report formats, or to use tools built into the software package to create specialized reports that arrange data in any number of ways. This versatile data warehouse architecture makes it simple for corporations of different sizes and related to different fields to use the same basic software, but adapt the usage of that software to fit their individual needs.

Most real time data warehousing packages also allow for the generation of reports on-demand as well as on a set schedule. This can be extremely helpful, as it is possible to submit a query on the spur of the moment, and have an answer within a matter of seconds or minutes. For example, if a sales manager is presented with a question about the current day’s sales of a given product line, he or she can simply frame the query, have the software pull the up-to-the minute data, and have a report that provides the information with ease. More conventional methods would require up to a half-hour or more to manage what the real time warehousing can manage in less than three minutes.

In an era when even a few minutes can mean the difference between success and failure, the use of real time data warehousing not only provides a simple means of storing all relevant data in a common repository. This approach also means having the information on hand at any time to make decisions with the potential to increase the bottom line for any company. For this reason, many businesses, both large and small, are integrating real time software into their overall business intelligence archiving and retrieval processes.


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Post 1

We have been implementing real-time or near real-time datawarehouses for seven years now. We are using our own product Invantive Producer, based on Oracle RDBMS technology.

One of the biggest things I am having problems with is the aspect of world time. When the feeding cycles shorten as systems present their data on business occurrences, the accuracy required to match the feeds increases. Of course, part of it can be solved by using a protocol such as ntp, but nonetheless the feeding cycles differ.

Fast systems (for instance, really real-time) deliver their feeds earlier then systems that integrate their data into the real-time datawarehouse once every hour.

Does anyone have practical tips on how to combat these real-time integration issues and deliver accountable reports?

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