Assess and govern BigQuery datasets, routines, scheduled queries, data movement, validation, and cutover.

BigQuery to Databricks

The BigQuery pack inventories datasets, tables, nested schemas, views, routines, scheduled queries, BigQuery ML, remote functions, authorized views, and downstream BI. It combines specialist conversion and transfer tools with Airlift's governed ledger and independent evidence model.

fa source inspect bigquery
fa source plan bigquery
fa source plan bigquery --json > .airlift/bigquery-plan.json

Migration route

Stage	Implementation	Airlift value
Assess	Export metadata and source artifacts; run the admitted analyzer route	Accepted inventory, dependencies, exclusions, immutable report refs
Convert	Translate supported GoogleSQL and disposition scripting, ML, remote functions, and authorized-view behavior	Versioned attempts, artifact digests, warnings, residue ownership
Transfer	Use restartable storage exports or an admitted connector with timestamp/change catch-up	Manifests, watermarks, restart, lag, reconciliation
Validate	Test nested/repeated fields, numeric and timestamp semantics, partitions, access, and business queries	Independent readiness evidence
Cut over	Freeze the wave and change consumers with checkpoint/apply-once/verify	Approvals and verifiable outcome or rollback
Modernize	Adopt Delta, liquid clustering, Lakeflow, Unity Catalog, and serverless SQL	Separate, independently validated release

The pack is assessable: it describes an admitted assessment route, but that does not certify your transfer, validation, scale, or cutover. Produce a live manifest and run:

fa source certification-check bigquery-certification.json

Complete developer command sequence

Generate this exact recipe from the installed CLI so the guide and executable surface stay in sync:

fa source recipe bigquery
fa source recipe bigquery --variant bigquery --json > .airlift/bigquery-recipe.json

The App is engagement-aware. Google BigQuery appears under Active sources only after the source estate is added to an active engagement. The menu is derived from governed engagement scope; installing Airlift does not expose unrelated source pages.

Every remote mutation below requires --host, --org, authenticated workspace identity, and a stable --idempotency-key. JSON request files contain identifiers, artifact references, and opaque credential references—never passwords, tokens, or connection strings. Run fa <resource> <operation> --help for the current schema and exit semantics.

0. Inspect the source contract

fa source inspect bigquery --json > .airlift/bigquery-profile.json
fa source plan bigquery --variant bigquery --json > .airlift/bigquery-capability-plan.json

Expected artifacts:

.airlift/bigquery-profile.json
.airlift/bigquery-capability-plan.json

Open Engagements → active engagement in the App. This stage is visible at /engagements after replacing the placeholder ID with the governed engagement ID.

1. Create governed scope and connection references

fa engagement create --file engagement.json --idempotency-key migration-create-v1
fa estate register --file bigquery-estate.json --idempotency-key bigquery-estate-v1
fa connection register --file bigquery-connection.json --idempotency-key bigquery-connection-v1
fa engagement update --file bigquery-scope.json --idempotency-key bigquery-scope-v1
fa engagement preflight <engagement-id>

Expected artifacts:

Governed engagement
Source estate
Opaque connection binding

Open Engagements → active engagement in the App. This stage is visible at /engagements/<engagement-id> after replacing the placeholder ID with the governed engagement ID.

2. Assess and accept inventory

fa assessment start --file bigquery-assessment-start.json --idempotency-key bigquery-assessment-start-v1
fa assessment status <assessment-id> --json
fa assessment record --file bigquery-assessment-record.json --idempotency-key bigquery-assessment-record-v1
fa assessment accept --file bigquery-assessment-accept.json --idempotency-key bigquery-assessment-accept-v1
fa inventory list --estate-id <estate-id> --json

Expected artifacts:

Assessment report reference
Normalized inventory
Dependency graph

Open Engagements → active engagement in the App. This stage is visible at /engagements/<engagement-id>/sources/bigquery after replacing the placeholder ID with the governed engagement ID.

3. Implement the admitted source adapter

No source-specific executable compiler exists in this release. Continue with assessment, governed work, and an admitted adapter; Airlift does not invent executable output.

Expected artifacts:

Capability plan and adapter requirements only

Open Engagements → active engagement in the App. This stage is visible at /engagements/<engagement-id>/artifacts after replacing the placeholder ID with the governed engagement ID.

This source is cataloged or assessable but has no source-specific executable compiler in the installed Airlift generation.

4. Convert, move, and remediate

fa plan generate --file bigquery-migration-plan.json --idempotency-key bigquery-plan-v1
fa conversion batch create --file bigquery-batch.json --idempotency-key bigquery-batch-v1
fa conversion batch start --file conversion-batch-start.json --idempotency-key conversion-start-v1
fa residue list --engagement-id <engagement-id>
fa transfer plan --file bigquery-transfer.json --idempotency-key bigquery-transfer-v1
fa transfer run <transfer-id> --idempotency-key transfer-run-v1
fa transfer reconcile <transfer-id> --idempotency-key transfer-reconcile-v1

Expected artifacts:

Target artifacts
Residue cases
Transfer checkpoints
Reconciliation evidence

Open Engagements → active engagement in the App. This stage is visible at /engagements/<engagement-id>/runs after replacing the placeholder ID with the governed engagement ID.

5. Validate independently and inspect discrepancies

fa validation run --file bigquery-validation.json --idempotency-key bigquery-validation-v1
fa validation status <validation-execution-id> --json
fa discrepancy list --engagement-id <engagement-id>
fa artifact list --engagement-id <engagement-id>

Expected artifacts:

Provider run references
Readiness evidence
Discrepancies

Open Engagements → active engagement in the App. This stage is visible at /engagements/<engagement-id>/runs after replacing the placeholder ID with the governed engagement ID.

6. Certify, cut over, and export evidence

fa certificate list --object-id <object-id>
fa cutover status <wave-id> --json
fa evidence list --engagement-id <engagement-id>
fa evidence export --file bigquery-evidence-export.json --idempotency-key bigquery-evidence-export-v1

Expected artifacts:

Migration certificates
Cutover evidence
Content-digested evidence export

Open Engagements → active engagement in the App. This stage is visible at /assurance after replacing the placeholder ID with the governed engagement ID.

Runway executes releases; Experiments owns validation verdicts; Airlift owns migration readiness and cutover policy.

What developers see in the App

The contextual source workspace shows the accepted estate and the factory stages for this engagement. Artifacts displays immutable references, content digests, media types, and provider lineage. Runs displays assessment, conversion, transfer, validation, and deployment executions without treating a provider's success as an Airlift verdict.

Airlift Google BigQuery source workspace showing the governed estate and developer command entry point — Google BigQuery has its own engagement-scoped workspace; unrelated source systems are not shown.
What you are seeing
This public synthetic capture demonstrates Airlift setup and navigation for Google BigQuery; it is not evidence of a live connection or certified migration.
What to do next
Register the client estate, bind a credential reference, run the Google BigQuery assessment recipe, and admit the resulting evidence.
Read the developer workflow

These are automated captures from public synthetic engagements. The source workspace is specific to Google BigQuery; no unrelated source is presented as its migration journey. For sources without an evidence-backed journey, the image demonstrates setup, navigation, and developer entry points only—not a live connection, converted output, or certified migration. No client data, credentials, workspace hostnames, or internal deployment identifiers are embedded in the images.

BigQuery to Databricks

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