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, walks the lineage graph, and joins the incident it’s actually causing downstream — 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 onto the same graph as 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.