Freshness, volume, and schema-drift checks over the events your pipelines already emit, with lineage that shows exactly what each failure reaches.
Data tools stop at the warehouse. byteshift carries the broken table forward — to the model that trains on it and the revenue dashboard that reads it — so a missed SLA arrives with its blast radius.
Freshness, volume, and schema-drift monitors on every table and job — free-tier checks over the OpenLineage and dbt events you already produce. Zero matches is a real zero, never a false green.
Table- and column-level lineage from your dbt manifest and run events. Every edge is labeled declared, observed, or inferred — with the confidence and freshness that back it.
A freshness trip anchors to the table, follows the lineage downstream, and joins the incident it’s actually causing — the dashboard, the model, the customer-facing service.
A column drops on orders_raw; two hops later your support agent starts hallucinating and token spend spikes. byteshift joins all three into one incident along the lineage path, cause at the top.
Data quality isn’t a silo here — it’s the first domino, tracked right alongside the AI, services, and cost it moves.
Send the run events your pipelines already emit and your tables, jobs, and lineage appear — nothing new to instrument.