Evolving Data.
Dependable Reporting.
Manage schema changes originating in source systems, ETL pipelines, or the warehouse. AI agents automatically trace dependencies, prepare updates, and test downstream compatibility to protect BI reporting. Engineers approve production changes.
Staging → Intermediate → Final
Schema Changes, Business Consequences
A new field may be easy to ingest. A renamed key can break a join, alter a metric, or invalidate a dashboard. The proposed agent service connects schema detection to downstream impact and a controlled release.
Impact Across Every Layer
Trace changes across bronze, silver, and gold layers and through each data mart’s staging, intermediate, and final stages. Use lineage, SQL, and model definitions to identify the affected BI models, dashboards, and reports.
Compatibility Before Propagation
Compare changed schemas with approved contracts. Coordinate updates to target tables, transformations, and dependent models in dependency order. Preserve the fields and definitions downstream consumers need, with automated tests and engineer approval before production propagation.
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.
| Change | Agent Preparation | Release Check |
|---|---|---|
| New Columns | Draft target definitions, mappings, defaults, and tests. | Check required values, historical records, and downstream field selection. |
| Renamed Columns | Propose a mapping and compatibility alias; flag uncertain matches. | Confirm meaning with the owner. Test joins, models, and BI references. |
| Dropped Columns | Identify consumers and propose a deprecation or replacement plan. | Obtain owner approval. Protect retained data and dependent metrics. |
| Type Changes | Assess conversion, precision, range, and platform support. | Test widening and casts; block lossy conversions until resolved. |
| Contract Changes | Identify new nullability, nested fields, and required-value rules. | Verify business constraints and route incompatible records for review. |
One Reviewed Change Package
The agent coordinates the investigation and prepares the work. Production schema modifications remain behind an explicit human approval gate.
Detect & Compare
Monitor source schemas, ETL output contracts, and warehouse definitions. Compare versions to identify additions, removals, candidate renames, and type changes.
Map & Prepare
Identify dependencies and owners. Draft version-controlled changes for the affected layers, with compatibility and migration notes.
Test & Review
Run schema, transformation, reconciliation, and BI checks in an isolated environment. Present failures and unresolved dependencies to the engineer.
Approve & Release
Record approval against the tested version. Orchestrate downstream updates through the deployment workflow in dependency order. Verify BI refreshes and invoke the approved recovery plan if needed.
A Renamed Field.
A Protected Revenue Model.
A source replaces order_total with gross_amount. The agent flags a possible rename and traces its use from bronze ingestion through silver and gold models. It follows the revenue mart’s staging, intermediate, and final dependencies to the connected BI measures.
An engineer confirms whether currency, tax, and discount treatment are unchanged. The proposed patch keeps the existing reporting field available while the source mapping changes.
Reconciliation and dashboard-query tests must pass before release. If the meaning has changed, the agent escalates a metric-definition decision instead of treating it as a simple rename.
Platform-Aware Execution
Native schema evolution is one part of the solution. The agent service coordinates changes across components and consumers rather than assuming that an ingestion setting protects the whole platform.
Databricks
Connectors, parsers, streaming engines, and target datasets handle evolution separately. The implementation aligns schema tracking and target-table settings, with required restarts, refreshes, or rewrites included in the release plan.
Databricks Schema Evolution ↗Snowflake
Automatic table evolution supports new columns and removal of NOT NULL constraints in supported loading paths. Renames, column deletion, and arbitrary type changes require separate handling. The agent must not interpret missing source fields as permission to drop target columns.
Snowflake Schema Evolution ↗Production Safeguards
Approval applies to the exact tested change. The agent does not receive unrestricted production DDL access.
Traceable Decisions
Retain schema versions, proposed code, test results, approvals, and deployment events in an audit record. Separate investigation permissions from release permissions.
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 refreshes, queries, field bindings, and key measures. Unmapped reports remain an explicit review item; compatibility cannot be guaranteed for unseen consumers.
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.
