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copied!<p>With these fields you can't directly determine price drops or new application. You'll have to insert these in your own database, and determine the differences from there. In a relational database like MySQL this isn't too complex:</p> <p>To determine which applications are new, you can add your own column "first_seen", and then query your database to show all objects where the <code>first_seen</code> column is no longer then a day away.</p> <p>To calculate price drops you'll have to calculate the difference between the retail_price of the current import, and the previous import.</p> <hr> <p>Since you've edited your question, my edited answer:</p> <p>It seems like you're having storage/performance issues, and you know what you want to achieve. To solve this you'll have to start measuring and debugging: with datasets this large you'll have to make sure you have the correct indexes. Profiling your queries should helping in finding out if they do.</p> <p>And probably, your environment is "write once a day", and read "many times a minute". (I'm guessing you're creating a website). So you could speed up the frontend by processing the differences (price drops and new application) on import, rather than when displaying on the website.</p> <p>If you still are unable to solve this, I suggest you open a more specific question, detailing your DBMS, queries, etc, so the real database administrators will be able to help you. 60 million rows are a lot, but with the correct indexes it should be no real trouble for a normal database system.</p>

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