> Each of these AWS services is clearly and demonstrably less popular than the alternative it's compared against.
Subject to the limitations of the data, which is mostly what they can scrape from open sources with an unspecified weighting algorithm: https://db-engines.com/en/ranking_definition
That's important to pay attention to because there are many areas this can go wrong — for example, it doesn't include AWS support or Amazon's own Q&A forums so you know you're missing a certain fraction of highly-relevant activity and, more importantly, as a bulk data-mining exercise you have a big challenge differentiating breadth and depth. MongoDB was heavily promoted about a decade ago so there are a ton of SO questions from people who fired up a copy and were looking to use it — which is great, but it doesn't tell you how many of them ended up actually using it for something serious or how big their project was. A thousand hobbyists storing 1% of the number of records of a single enterprise customer is not really something you can easily distill down to a single value. It also doesn't tell you how well people are sticking with it — for example, mature development / ops teams tend to ask fewer basic questions (they've been resolved & in-house expertise means they might never hit StackOverflow) but they might post harder questions about scaling. Does that mean that use of the technology is tapering or that the community is maturing?
The other big question is how you account for managed services. For example, if I use Kinesis I'm outsourcing a lot of operations to AWS; if I use Kafka I have to bring that to the table — one of those scenarios is likely going to involve a LOT more questions and open activity which on its own doesn't tell you anything about how many applications or how much data I'm using it for in either case.
Subject to the limitations of the data, which is mostly what they can scrape from open sources with an unspecified weighting algorithm: https://db-engines.com/en/ranking_definition
That's important to pay attention to because there are many areas this can go wrong — for example, it doesn't include AWS support or Amazon's own Q&A forums so you know you're missing a certain fraction of highly-relevant activity and, more importantly, as a bulk data-mining exercise you have a big challenge differentiating breadth and depth. MongoDB was heavily promoted about a decade ago so there are a ton of SO questions from people who fired up a copy and were looking to use it — which is great, but it doesn't tell you how many of them ended up actually using it for something serious or how big their project was. A thousand hobbyists storing 1% of the number of records of a single enterprise customer is not really something you can easily distill down to a single value. It also doesn't tell you how well people are sticking with it — for example, mature development / ops teams tend to ask fewer basic questions (they've been resolved & in-house expertise means they might never hit StackOverflow) but they might post harder questions about scaling. Does that mean that use of the technology is tapering or that the community is maturing?
The other big question is how you account for managed services. For example, if I use Kinesis I'm outsourcing a lot of operations to AWS; if I use Kafka I have to bring that to the table — one of those scenarios is likely going to involve a LOT more questions and open activity which on its own doesn't tell you anything about how many applications or how much data I'm using it for in either case.