Why not let the data make the technology choices for you ?
The way I go about making technology choices is by examining the data that I'll be working with in conjunction the data access patterns inherent in the features that I'll need to support.
I look at things like projected read/write throughput, latency characteristics, total data volume, concurrency, and whether or not the problem domain actually requires highly relational queries.
I think that a lot of shops don't put enough thinking into figuring out what kind of data access patterns they'll need to support throughout the life-cycle of the business. This is no big deal if the product doesn't experience growth. But in terms of rich web applications which begin to experience growth the team inevitably ends up with a massive scaling problem unless their system architecture was designed to support these access patterns from the ground up.
It seems that this "growing pains" scaling nightmare has become almost a right of passage for successful tech startups. Founders are generally led to believe that it's a good thing for them to need to sell equity to outside investors in order to "scale out" a much larger team to build out the infrastructure required to perform in-flight rocket surgery on the application before it either explodes or becomes increasing cost inefficient.
While this whole process greatly benefits VCs, the high end tech engineering job market, and recruiters, it's absolutely terrible the founding team because it means they inevitably get massively diluted as a consequence of experiencing success. I'm not saying it's a conspiracy, but I am saying there is massive financial incentive to keep this kind of technical knowledge about best practices an open secret within the highly paid IT consultancy world.
TLDR: It's my supposition that small teams can build scalable, composable, systems by thinking about web scale data access patterns from the beginning.
I look at things like projected read/write throughput, latency characteristics, total data volume, concurrency, and whether or not the problem domain actually requires highly relational queries.
I think that a lot of shops don't put enough thinking into figuring out what kind of data access patterns they'll need to support throughout the life-cycle of the business. This is no big deal if the product doesn't experience growth. But in terms of rich web applications which begin to experience growth the team inevitably ends up with a massive scaling problem unless their system architecture was designed to support these access patterns from the ground up.
It seems that this "growing pains" scaling nightmare has become almost a right of passage for successful tech startups. Founders are generally led to believe that it's a good thing for them to need to sell equity to outside investors in order to "scale out" a much larger team to build out the infrastructure required to perform in-flight rocket surgery on the application before it either explodes or becomes increasing cost inefficient.
While this whole process greatly benefits VCs, the high end tech engineering job market, and recruiters, it's absolutely terrible the founding team because it means they inevitably get massively diluted as a consequence of experiencing success. I'm not saying it's a conspiracy, but I am saying there is massive financial incentive to keep this kind of technical knowledge about best practices an open secret within the highly paid IT consultancy world.
TLDR: It's my supposition that small teams can build scalable, composable, systems by thinking about web scale data access patterns from the beginning.