Real time data load solutions are used because of the advantages of feeding the data from the source system directly into the warehouse tables by insert or update. In some industries it is essential to base every decision on up-to-date data. Because of this, small response times are needed. Examples of industries where real time updates are essential are Financial Services or Transportation. The main disadvantage of real time data load solution is the cost that it implies. This is why near real time load data solutions were developed.
Near real time data warehousing is an efficient way to extract, process and analyze huge amounts of data on a time interval basis. This operational system can be implemented by setting up a small frequency at which the process is executed. This approach is using the traditional way of working, yet by making this load more frequent and optimizing it for small batch updates, achieves a near real time system that responds to the needs or problems that can occur.
The major difference between the two solutions is that in the real time system there is a continuous input, process and output of data, while in a near real time system you have a batch load and the transactions that are being collected over a period of time are first processed and only after that are the results produced.
Although near real time can not substitute real time, the cost advantages of this solution have to make every technology executive think if speed of response is essential and desirable in his or her business or just a caprice. If the answer is the latter, then near real time should be taken into consideration.
Gabi Stefan is a Business Intelligence developer at Qubiz