08-08-2012 09:12 AM
We use datawarehouse to store historical data,we can store huge volumes of data.But it should have limit.I have read that in DWH once entered data can not be erased.Is it true?
08-09-2012 09:05 AM
i didnt get you.We use datawarehouse to store historical data we can store huge volumes of data.I have read that in DWH once entered data can not be erased.Is it true?.
08-09-2012 09:30 AM
Of course you can. There is nothing magic about a data warehouse. It's more like a methodology. You usually have tool like SAS DI Studio for working with the data warehouse. The data itself can be stored in any database. As far as I know you can delete data from all databases.
08-09-2012 10:10 AM
I think that the idea behind this is that your operational systems maintain a current snapshot of the data; i.e. if you are a bank and your customer closes his account, you delete his information from your databases. In your data warehouse, by contrast, you want to keep all of your data; it might be very useful to study information about customers who close their accounts. Therefore, the general rule is that operational systems delete obsolete data in real time, while all operational data moves over to and is added to the data warehouse, which accumulates everything.
You can see that the most useful data warehouse would keep all of the data, as you can't predict what you might need to analyze at a later date. That being said, however, organizations have to balance the cost of maintaining the data with the benefits they derive from them. Various strategies are used to purge data from warehouses. A common one is to delete the oldest data, based on the thinking that current data is the most useful. The warehouse then becomes a "rolling snapshot" of the company's operations. Other options exist, though. One is to summarize data; instead of keeping the full address for a customer, just retain the ZIP as being useful enough. Another is to stage the data, keeping the most useful on fast (and expensive) disk, and moving less useful data to slower and cheaper storage.
There are very few hard and fast rules in BI, but there are useful principles that you can use to make cost-effective decisions.