Agentic Schema Evolution

Evolving Data.
Dependable Reporting.

Manage schema changes across source systems, pipelines and data platforms. Rudder Analytics builds AI agents that trace dependencies, prepare updates and test downstream compatibility. Engineers approve production releases.

Discuss Schema Change Risks Explore the Approach
AI-Guided Schema Change
SourcesETL PipelinesLakehouse / Warehouse
AI Agent
  1. Detect
  2. Map Impact
  3. Prepare & Test
BronzeRaw ingestion
SilverValidated models
GoldBusiness models
Data Mart Dependencies

Connected transformations and reporting models

BI Models, Dashboards & Reports
Engineer ApprovalRelease the tested change through CI/CD.
Changes follow the dependencies in your data platform.

Schema Changes, Business Consequences

Source systems add, rename and remove fields, often without notice. A new field may be easy to ingest. A renamed key can break a join, change a revenue figure or leave a dashboard quietly wrong.

From Field to Dashboard

  • Traces a changed field through ingestion, transformations and data marts
  • Finds the tables, measures and reports that depend on it
  • Uses lineage, SQL and model definitions

Compatibility Before Release

  • Compares the change with approved data contracts
  • Prepares updates to target tables, transformations and dependent models
  • Preserves the fields and definitions existing reports use

A Response for Each Change

Classify the change before choosing a migration. Similar column names do not prove a rename, and a technically valid type change can still alter business meaning.

  • New Columns

    Agent Preparation

    Draft target definitions, mappings, defaults and tests.

    Release Check

    Check required values, historical records and downstream field selection.

  • Renamed Columns

    Agent Preparation

    Propose a mapping and compatibility alias; flag uncertain matches.

    Release Check

    Confirm meaning with the owner. Test joins, models and BI references.

  • Dropped Columns

    Agent Preparation

    Identify consumers and propose a deprecation or replacement plan.

    Release Check

    Obtain owner approval. Protect retained data and dependent metrics.

  • Type Changes

    Agent Preparation

    Assess conversion, precision, range and platform support.

    Release Check

    Test supported conversions; hold changes that could lose data for resolution.

  • Contract Changes

    Agent Preparation

    Identify changes to nullability, nested structures and required-value rules.

    Release Check

    Verify business constraints and route incompatible records for review.

One Reviewed Change Package

The agent brings proposed code, impact analysis and test evidence into one review package. An engineer approves the tested version before release.

  1. 01

    Detect & Compare

    Monitor source schemas, ETL output contracts and warehouse definitions. Compare versions to identify additions, removals, candidate renames and type changes.

  2. 02

    Map & Prepare

    Identify dependencies and owners. Draft a pull request for the affected code and migration changes, with compatibility and deployment notes.

  3. 03

    Test & Review

    Run schema, transformation, reconciliation and accessible BI checks in an isolated test environment. Present failures and unresolved dependencies to the engineer.

  4. 04

    Approve & Release

    Record approval against the tested version. Deploy affected changes in dependency order. Verify pipeline runs, data results and BI refreshes; follow the approved recovery plan if checks fail.

A Renamed Field.
A Protected Revenue Model.

  1. AI Agent

    Flags a possible rename when order_total becomes gross_amount and traces it to the revenue reports.

  2. Data Owner

    Confirms whether currency, tax and discount treatment are unchanged. The patch keeps the existing reporting field available.

  3. Release Check

    Reconciliation and dashboard tests must pass first. A changed meaning becomes a metric-definition decision, not a rename.

Platform-Aware Execution

Native schema evolution handles specific changes within a platform. Rudder Analytics coordinates the mappings, tests and downstream updates needed across the wider data stack.

Databricks

Coordinate schema changes across ingestion, parsing, streaming queries and target tables. Include any required restarts, refreshes or rewrites in the deployment plan.

Databricks Schema Evolution ↗

Snowflake

Use automatic table evolution to add columns and remove NOT NULL constraints where supported. Handle renames, column removals and type changes through reviewed migrations. A missing source field does not authorize deleting a target column.

Snowflake Schema Evolution ↗

Production Safeguards

Approval applies to the exact tested change. Investigation access is separate from the permissions used to deploy it.

Traceable Decisions

Retain schema versions, proposed code, test results, approvals and deployment events in an audit record.

Recoverable Releases

Plan code reversion and data recovery together. Snapshots, retention windows, backups and replay determine what can be restored; a code rollback alone cannot recover deleted data.

Visible Coverage Gaps

Register BI dependencies and test accessible refreshes, queries, field bindings and key measures. Include unmapped reports and missing access in the release review.

Start with one critical pipeline and its downstream reports. The engagement defines supported change types, approval owners, test coverage and recovery procedures before extending across the data platform.

Controlled Change. Clear Accountability.

Bring a recurring schema issue or a critical reporting dependency. Define the changes an agent can prepare and the evidence required for release.

Discuss Your Requirements