Data change intelligence

See the impact
before it ships.

Veyra traces the hidden impact of data changes across your stack — before they reach production.

Powered by DataHub

The problem

Data changes are never
just data changes.

One schema change can ripple through pipelines, dashboards, models and teams you didn't know depended on it.

1 asset

Impact analysis

VEYRAanalysis · run 41c9

Asset

analytics.customers

Proposed change

Change customers.email from STRING to nullable STRING

6
affected assets
3
teams
2
dashboards
1
ML model

Risk factors

  • Production dashboard dependency
  • ML dependency
  • Multiple downstream datasets

High risk

87

risk score

Lineage

  • customerscustomer_360Customer Dashboard
  • customersorders_enrichedRevenue Dashboard
  • customerscustomer_featuresChurn Model

Recommendation

Backfill nulls in customer_360 and notify Data Platform before applying the schema change.

The solution

Give your AI context.

Veyra combines schema, lineage, ownership, dependencies and ML context into a single actionable impact analysis.

SchemaLineageOwnershipDependenciesML context
  1. 01DataHub
  2. 02Context
  3. 03Veyra Agent
  4. 04Impact analysis
  5. 05Engineering decision

AI analysis

From metadata to decisions.

  1. 01

    Retrieve context

    Metadata, schemas and asset history pulled from DataHub.

  2. 02

    Trace lineage

    Column-level paths followed through every downstream hop.

  3. 03

    Identify ownership

    Owning teams and on-call contacts resolved per asset.

  4. 04

    Calculate blast radius

    Assets, dashboards and models scored by exposure.

  5. 05

    Generate rollout plan

    An ordered, reviewable sequence for shipping safely.

Integration

Built on context.

Veyra uses DataHub to understand the relationships hidden inside your data stack.

  • DataHub metadata
  • Lineage
  • Ownership
  • ML context
VEYRA

The result

Know what breaks.
Before you change it.

87

High risk

6
downstream assets
3
teams
1
ML dependency

Recommended rollout

  1. 1Notify Data Platform
  2. 2Update downstream transformation
  3. 3Validate production dashboard
  4. 4Validate ML features
  5. 5Apply schema change

Every step is traceable back to the asset that required it.

Ship changes
with context.

Know the blast radius before production finds it for you.