Data scientists spend up to 80% of their time cleaning, integrating, and preparing data before any analysis can begin, a figure that has stayed stubbornly high for over a decade. Insight work gets delayed, AI models sit idle, and dashboards surface numbers leadership cannot fully trust. The cause, in most cases, is data arriving in formats that downstream tools cannot
Most production data loss isn’t caused by platform failures or hardware. It comes from a pipeline that silently overwrites a table with bad data, an engineer who runs a DELETE in the wrong environment, or a CREATE OR REPLACE that wipes out three months of history on a Friday afternoon. By the time the problem is noticed, the stakeholders are
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
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