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gbn brings up some good approaches. I think the "right" longer term answer for you is the t3rd option, though. It sounds like you have two business use cases of your data - 1.) Real time Online Transaction Processing (OLTP). This is the POS transactions, inventory management, quick "how did receipts look today, how is inventory, are we having any operational problems?" kind of questions and keeps the business running day to day. Here you want the data necessary to conduct operations and you want a database optimized for updates/inserts/etc. 2.) Analytical type questions/Reporting. This is looking at month over month numbers, year over year numbers, running averages. These are the questions that you ask as that are strategic and look at a complete picture of your history - You'll want to see how last years Christmas seasonal items did against this years, maybe even compare those numbers with the seasonal items from that same period 5 years ago. Here you want a database that contains a lot more data than your OLTP. You want to throw away as little history as possible and you want a database largely optimized for reporting and answering questions. Probably more denormalized. You want the ability to see things as they were at a certain time, so the Type 2 SCDs mentioned by gbn would be useful here. It sounds to me like you need to create a reporting database. You can call it a data warehouse, but that term scares people these days. Doesn't need to be scary, if you plan it properly it doesn't have to take you 6 years and 6 million dollars to make ;-) This is definitely a longer term answer but in a couple years you'll be happy you spent the time creating one. A good book to understand the concept of dimensional modeling and thinking about data warehouses and their terminology is <a href="http://rads.stackoverflow.com/amzn/click/0471200247" rel="nofollow">The Data Warehouse Toolkit</a>.
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1. POHow to handle archiving for seasonal database values on SQL Server
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1. USMike Walsh
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