Every retrieval-augmented chatbot, every “find me something similar” feature, and every recommendation that feels like it read your mind runs on the same quiet engine underneath: vector search. The hard part was never the idea. It was keeping a separate vector database in sync with the data that actually changes in your warehouse, paying for infrastructure that drifted out of
Most teams discover dbt and Databricks separately. They adopt Databricks for the compute, the Delta tables, and the lakehouse, then watch their transformation logic sprawl across hundreds of notebooks that nobody can fully trace. dbt is the layer that brings order to that sprawl, and the dbt-databricks adapter is what lets the two work as one system. Run together, dbt
The hardest bug in production machine learning is rarely in the model. It is in the gap between the features a data scientist computed in a notebook to train the model and the features the application computes at request time to score it. A churn model that saw a clean “average order value over the last 30 days” during training
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