Real Time Data Warehousing
Traditionally data warehousing systems are only able to be loaded with historical data. The historical data in a data warehouse at its best is only loaded with data a week or so old. Because business is a fast paced environment that requires fast decision making, management often times find themselves wanting and needing to be able to work with real time data that is both fresh and new. Real time data warehousing offers a completive advantage to businesses in the market. Real time data warehousing can help a business bridge the gap between historical knowledge and immediate knowledge.
Traditional data warehousing before real time data warehousing
Traditional data warehousing seems to be a trend of the past as more and more businesses and operations begin to demand real time data warehousing. Unlike real time data warehousing, traditional data warehousing is still very useful to some that do not necessarily need the most up to date data entered into the data warehouse. One example could be the way some government entities use the system. They may for example be using the system when censes are conducted, not an everyday occurrence. Some businesses still use traditional data warehousing for their operation, but as more and more realize the importance of real time data warehousing this is beginning to change.
Why real time data warehousing?
Real time data warehousing is proving to be the future of data warehousing. It will be only a matter of time when real time data warehousing replace the traditional data warehousing model. The reasons are simple, real time data warehousing is faster, allowing the business to make quicker informative decisions based on the real time and historical data present in the real time data warehouse. Businesses find this extremely attractive because the quicker they are to respond to certain trends in the market the more successful they will more than likely be.
Real time data warehousing challenges
Traditional data warehousing has a number of challenges and real time data warehousing only add to those challenges already faced with. One of the biggest challenges users of real time data warehousing will be faced with is real time extraction, transformation, and loading. Because data loading is usually done at a time when no users can access it the problem arises as to how to up load real time data as it comes in and still be able to access the system to report and analyze the data. This is obviously a problem for the users of the real time data warehousing system.
Real time data warehousing solutions
Fortunately there is a solution to this real time data warehousing challenge. The best way to deal with the challenge of not being able to access the warehouse because of constant updating data is known as near real time ETL. Near real time ETL is the process of updating the data warehouse with new data only a few times a day. Instead of the warehouse constantly being bombarded with new data every second, in turn it makes it unavailable for use. The system can be updated several times a day instead.