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Schema-drift-aware orchestration

Pipelines that don't break when schemas do

Queryvine watches your upstream sources for schema changes and automatically reroutes, remaps, or pauses pipelines before broken data reaches your dashboards.

< 800ms avg detection latency
500k+ events/sec per pipeline
50+ source connectors
The monday-morning problem

Monday mornings are broken by Friday deploys

When a vendor renames a column, your warehouse connector doesn't know. Your pipeline still runs. Your dashboard silently zeroes out. By Monday your data team is debugging history instead of building features.

Most orchestrators assume schemas stay stable. They handle retries, scheduling, and dependencies — but they don't watch for the field-level changes that silently corrupt your data.

How Queryvine works

Schema drift caught at the edge, not the dashboard

Three components that work together to keep your pipelines healthy regardless of what changes upstream.

01

Continuous schema inspection

Queryvine polls every source on configurable cadence and fingerprints the schema. New field? Renamed column? Type widening? Detected in one cycle.

02

Adaptive routing rules

Define drift responses in YAML: remap, skip, pause, or alert. Rules are version-controlled alongside your pipeline definitions.

03

Clean data forward

Downstream consumers receive consistent schemas regardless of what changed upstream. Dashboards stay green.

Capabilities

Built for teams running 50+ sources

Every feature designed around the reality that upstream schemas will change — without warning, without ceremony.

Visual DAG editor

See your entire pipeline graph. Hover any node to inspect schema version, last-run status, and drift history at a glance.

Config-as-code

Pipelines are YAML definitions in your repo. Version-controlled, PR-reviewable, CI/CD deployable — no click-ops required.

Drift alerts

Slack, PagerDuty, or webhook notifications when a source schema changes — before a single bad row reaches storage.

50+ connectors

Snowflake, BigQuery, Redshift, Kafka, dbt, Postgres, MySQL, S3, Fivetran-compatible — pluggable connector SDK for custom sources.

Works with your existing stack
From the field

Data engineers who've been there

"We used to spend every Monday morning tracking down which upstream column rename caused the weekend dashboard outage. Queryvine automated the detection and the remapping. Our on-call rotation is quieter."

Jordan Castellano
Principal Data Engineer — Nerthex Analytics

"The YAML-first config approach meant our infra team could review pipeline changes in the same PR workflow as everything else. No more 'who changed the pipeline' Slack threads."

Anika Svensson
Analytics Engineering Lead — Veloris Data
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