Live versus Stored Data

I’ve been working with various technologies as experiments over the last few years. I’m curious how useful I find them, as well as how they might help the clients and customers I run into at Redgate Software.

I think Jupyter notebooks are a technology that has a lot of promise, and their use is growing in many organizations. One of the interesting things with notebooks is they can store results inside them, which allows a transfer of information in an interesting fashion. If I re-run a cell, the results can change and comparing them is challenging, but that is a separate issue. At least I can capture the results and share them.

I’ve also been working with Power BI at times. I saw a demo of a query from PBI to Excel, where the data was then stored in the PBIX file. This query was disconnected, but it could be refreshed. You can also configure how this works, so you get the choice of live or stored data.

If you are distributing information to users, those of us in the database world often think about having live data available and queried from a database, but often the same data gets queried over and over, which creates a load on our system. I know the data is often cached in these situations, but cache is a previous resource, so limiting the repeat queries can be valuable.

I do think this is a decision point for some applications, where we might choose to limit the amount of live data v stored data. There are times when speed matters more than having exact data, so cached or stored data works well. Sometimes the most current data is critical, and you need to query the database.

How do you decide when you want a live connection to a database, and when stored data is acceptable. I don’t know that I have any rules, but I evaluate each situation and try to work with users to make a decision. I suspect most people do the same thing, but if you always use live data, let me know today.

Steve Jones

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