I was recently involved in a query tuning work where we used synthetic, rather than production data, to validate the results of our query and index tuning work. We faced some issues with the generated data that had quite a severe impact on our testing, and that prompted me into writing this blog post. Lets start by first defining what is synthetic data. In my view synthetic data is data that resembles actual production data, but is artificial/generated. I have seen similar (and also more detailed) definitions elsewhere and I think it is a good one.
I also like to point out that there are plenty of good reasons for using synthetic data in testing, as production data is often strictly regulated and not easily available for testing purposes. However, you need to be certain that the synthetic data you are using is similar to what you have in production.
As we are almost done with the year 2017 it is a good time to look back for a moment, and to also consider what the next year will bring. This year has seen a fair share of hype around the topics of AI and Blockchain, even I touched that 
