Abstract
In large enterprises, huge amounts of data are generated and consumed, and only substantial fractions of the data change rapidly. Decision makers need up-to-date information to make timely and sound business decisions. Unfortunately, conventional decision support systems do not provide the low latencies needed for decision making in this rapidly changing environment. The decision making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in time. The typical update patterns for traditional data warehouses on an overnight or even weekly basis increase this propagation delay. Keeping data current by minimizing the latency from when data is captured until it is available to decision makers in this context is a difficult task.
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