Let me take a different angle here. According to Gartner the database market size is a 60+ BILLION dollar industry. If there's going to be one perfect database in 2023, then there are a bunch of companies out there spending a lot of resources on what surely seems like a hopeless cause.
Hey there is no doubt we are seeing convergence in the industry as each product works to scale horizontally and adopt different data models (relational, search, document, wide-column, key-value, Realtime, timeseries, and graph.) One thing the industry is learning from each other is that each model has an appropriate use case, and no one model is the "best".
Sure Moore's law and the cloud will eventually make some of these decisions less relevant, but never irrelevant.
Cloud providers creating back-ends as services with pay as you go auto scale - like firebase - are what will really make databases irrelevant to programmers. And I for one, welcome it. I'd rather build interesting applications and spend my time on user experience than coding up DTOs, repository patterns, REST endpoints and graphQL interfaces so that I can self host on Kubernetes or swap out my back end databases on a whim. Been there, done that. Anyone is right to argue that using these services create lock in - especially if your code is not designed to be loosely coupled - but the real question is - why should it matter in 2022?
There will always be a need for SQL and data engineering, for example shipping data changes in real time from cloud firestore into some other database more conducive to ad-hoc analytics. But these skills will not be required of full stack developers of the future.
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Let me take a different angle here. According to Gartner the database market size is a 60+ BILLION dollar industry. If there's going to be one perfect database in 2023, then there are a bunch of companies out there spending a lot of resources on what surely seems like a hopeless cause.
Hey there is no doubt we are seeing convergence in the industry as each product works to scale horizontally and adopt different data models (relational, search, document, wide-column, key-value, Realtime, timeseries, and graph.) One thing the industry is learning from each other is that each model has an appropriate use case, and no one model is the "best".
Sure Moore's law and the cloud will eventually make some of these decisions less relevant, but never irrelevant.
Cloud providers creating back-ends as services with pay as you go auto scale - like firebase - are what will really make databases irrelevant to programmers. And I for one, welcome it. I'd rather build interesting applications and spend my time on user experience than coding up DTOs, repository patterns, REST endpoints and graphQL interfaces so that I can self host on Kubernetes or swap out my back end databases on a whim. Been there, done that. Anyone is right to argue that using these services create lock in - especially if your code is not designed to be loosely coupled - but the real question is - why should it matter in 2022?
There will always be a need for SQL and data engineering, for example shipping data changes in real time from cloud firestore into some other database more conducive to ad-hoc analytics. But these skills will not be required of full stack developers of the future.