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Leveraging Information and Intelligence

David Linthicum

Three Categories of Data Latency

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There are three categories of data latency when it comes to BI:


1.         Real-time

2.         Near-time

3.         Some-time


Real-time data is precisely what it sounds like - information that is placed in the database as it occurs, with little or no latency.  Monitoring stock price information through a real-time feed from Wall Street is an example of real-time data.  Real-time data is updated as it enters the database, and that information is available immediately to anyone, or any application, requiring it for processing.


While zero latency real-time is clearly the goal, achieving it represents a huge challenge.  In order to achieve something near zero latency, BI implementation requires constant returns to the database, application, or other resource to retrieve new and/or updated information.  In the context of real-time updates, database performance must also be considered; simultaneous to one process updating the database as quickly as possible, another process must be extracting the updated information. 


Near-time data refers to information that is updated at set intervals rather than instantaneously.  Stock quotes posted on the Web are a good example of near-time data.  They are typically delayed twenty minutes or more, since the Web sites distributing the quotes are generally unable to process real-time data.  Near-time data can be thought of as "good-enough" latency data.  In other words, data only as timely as needed.


Although near-time data is not updated constantly, providing it still presents many of the same challenges as real-time data, including overcoming performance and management issues.


Some-time data is typically updated only once.  Customer addresses or account numbers are examples of no-time information.  Within the context of a BI computing architecture, the intervals of data copy, or data movement, do not require the kind of aggressiveness needed to accomplish real-time or near-time data exchange.

Industry expert Dave Linthicum tells you what you need to know about building efficiency into the information management infrastructure

David Linthicum

David Linthicum is the CTO of Blue Mountain Labs, and an internationally known distributed computing and application integration expert. View more


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