Why Are Databases Underestimated in Machine Learning (Part One)?

The ML Data Problem

Machine learning teams spend 80% of their time on data work: gathering, cleaning, transforming, and serving. The models themselves are often the easy part. The hard part is the data infrastructure.

Yet most ML infrastructure discussions focus on model training, hyperparameter tuning, and deployment. Databases are treated as a solved problem—just pick one and move on.

This oversight is costly. The choice of data infrastructure fundamentally constrains what ML applications are possible and how reliably they operate.

Why Databases Get Overlooked

Several factors contribute to databases being underestimated in ML. First, ML education focuses on algorithms, not infrastructure. Second, early prototypes don't need sophisticated data layers. Third, the pain of poor data infrastructure emerges slowly, then suddenly.

By the time teams recognize the problem, they've already accumulated technical debt: ad hoc pipelines, inconsistent features, and brittle serving layers.

The Real Requirements

ML applications have specific data requirements that differ from traditional analytics. They need: feature computation with consistent semantics across training and serving, low-latency retrieval for real-time inference, vector storage and similarity search for embedding-based models, and temporal consistency for features that depend on time-windowed aggregations.

No single traditional database provides all of these capabilities. Teams end up assembling a stack from multiple systems, each optimized for one piece of the puzzle.

The Context Lake Approach

Context Lakes address these requirements natively. They combine row and columnar storage for efficient feature computation. They support vector operations for embedding-based retrieval. They provide streaming ingestion for real-time features.

Most importantly, they unify these capabilities under a single system with consistent guarantees. Features computed for training match features served for inference by construction, not by careful engineering.

Conclusion

Databases are the foundation of ML infrastructure, not an afterthought. The choice of data layer determines what's possible, how reliably it works, and how much effort is required to maintain it.

In Part Two, we'll examine the practical implications of the fragmented stack and how consolidation changes the game.