This architecture still looks OK when the lion’s share of processing remains in SQL and AI plays a supporting role. When these roles become equal or shift toward AI, maintaining two separate data processing towers becomes untenable.
The inefficiencies of current practices (as outlined above) are the pull driver of the paradigm shift, while the push comes from the rapid evolution of foundational models that now can:
(no more custom ML models from scratch)
Eliminate many big data requirements as they are amortized by foundational models
Require less domain expertise (fine-tuning is iran rcs data much simpler than architecting)
Reduce overall ML/AI costs (API calls are cheap when factoring in savings from not owning the training and inference infrastructure)
As we transition from wrangling structured data in SQL to crunching unstructured data in deep learning models, some things in the “modern stack” obviously will become obsolete.
Let me explain.
Sure, vectorized operations over structured data are not going away. But, the key innovation is that unstructured data objects should be accessible right from where they live – which is cloud storage. There is no need to extract audio from mp3 files, store it as binary columns, and replicate it in every table iteration. Cloud storage is a perfectly acceptable abstraction for unstructured data.
There is also little reason to cling to SQL given it was born to query columnar data and has nothing to do with ML. If data crunching shifts toward running large foundational models via APIs and local AI helpers on GPUs, then SQL drops in importance. The entire data warehouse can perfectly work under the guidance of Python – or any other data language of the future – and manage the compute resources automatically.