gcp-live-bigquery-dataset-deletion-guard

name	gcp-live-bigquery-dataset-deletion-guard
description	Gate BigQuery dataset deletion, table truncation, and authorized view changes against a full downstream dependency audit and export confirmation. Dataset deletion is immediate and permanent with no recycle bin — this guard ensures no dataset is deleted without enumerating all tables, scheduled queries, Data Transfer jobs, Looker connections, and Dataflow pipelines that depend on it.
allowed-tools	Read Grep Glob
metadata	{"author":"github: Raishin","version":"0.1.0","updated":"2026-05-08","category":"data"}

GCP Live BigQuery Dataset Deletion Guard

Purpose

Act as the guarded live GCP operator for gcp-live-bigquery-dataset-deletion-guard work. Gate every BigQuery dataset deletion, table truncation, and authorized view change with a complete downstream dependency audit and export confirmation. Dataset deletion in BigQuery is immediate and permanent — there is no recycle bin, and all downstream pipelines break the moment the dataset is gone.

When to Use

Use this skill when:

• A BigQuery dataset is being deleted or a request to delete one is received
• A BigQuery table is being truncated (all rows deleted, schema preserved)
• An authorized view configuration is being changed or removed
• A scheduled query referencing the dataset needs to be decommissioned first
• A Data Transfer Service job is being removed alongside its target dataset
• An operator needs to enumerate all resources and dependencies before a BigQuery decommissioning

When NOT to Use

Do not use this skill when:

• The task is creating a new dataset or table with no existing data at risk
• The task is a read-only schema inspection or data quality audit with no deletion intent
• The task involves Cloud Storage, Cloud SQL, or other non-BigQuery data stores
• The task is modifying query logic or views without deleting any underlying data

Pre-Flight Checklist

Before executing any BigQuery deletion or truncation, verify all of the following:

1. Dataset identity confirmed — run bq show --format=prettyjson <PROJECT>:<DATASET> and confirm the dataset name, project, and location match the intended target.
2. Dataset inventory captured — run bq ls <PROJECT>:<DATASET> to enumerate all tables, views, and external tables. Document the table count and total bytes stored.
3. Scheduled queries audited — run bq ls --transfer_config --transfer_location=<LOCATION> --project_id=<PROJECT> to list all scheduled queries and Data Transfer jobs that reference this dataset.
4. Authorized views audited — check all authorized views that grant access to this dataset; identify any cross-project views that will break.
5. Downstream pipeline dependencies confirmed — check Dataflow jobs, Looker/Looker Studio connections, and any application code that references the dataset for active dependencies.
6. Export/backup confirmed — for production datasets, confirm that a full export to Cloud Storage (Avro, Parquet, or JSON) has been completed and verified before deletion is authorized.
7. Data retention policy reviewed — confirm there are no legal hold, compliance retention, or regulatory requirements that prevent deletion.

Required Confirmation

The operator must explicitly state all of the following before any deletion is executed:

• "I confirm the target is dataset <DATASET_ID> in project <PROJECT_ID>, location <LOCATION>."
• "I have reviewed the dataset inventory: <N> tables, <N> views, approximately <X> bytes."
• "I have audited downstream dependencies and confirmed all scheduled queries and DTS jobs have been decommissioned or will tolerate this deletion."
• "I confirm that an export/backup has been completed (or that no backup is required with documented justification)."
• "I approve this deletion."

Execution Steps

1. Capture full dataset inventory and downstream dependency audit.
2. Confirm active principal has roles/bigquery.dataOwner for the target dataset.
3. Present the dataset inventory, dependency findings, and export status to the operator for explicit approval.
4. Execute the mutation:
   • Delete dataset (with all contents): bq rm -r -f <PROJECT>:<DATASET>
   • Delete a single table: bq rm -f <PROJECT>:<DATASET>.<TABLE>
   • Truncate a table (delete all rows): bq query --nouse_legacy_sql 'TRUNCATE TABLE \..`'`
   • Update authorized views: bq update --source <JSON_FILE> <PROJECT>:<DATASET>
   • Confirm the deletion is reflected with bq ls <PROJECT>: (dataset should not appear).

   Rollback Procedure

   • Dataset deletion (NOT reversible): BigQuery dataset deletion is immediate and permanent. There is no recycle bin. If deletion was accidental, the only recovery path is restoring from a Cloud Storage export.
   • Table deletion (partially recoverable): If the table had a default expiration set, it may appear in the BigQuery __TABLES__ metadata with an expiration timestamp — but this does not allow recovery after deletion.
   • Table truncation (NOT reversible without backup): Truncation removes all rows. Recovery requires restoring from an export or a snapshot.
   • For accidental deletion, immediately open a GCP Support case and provide the dataset resource path and deletion timestamp from Cloud Audit Logs.

   Post-Change Verification

   1. Run bq ls <PROJECT>: — confirm the deleted dataset no longer appears.
   2. Check Cloud Audit Logs for the deletion event: gcloud logging read 'protoPayload.methodName="google.cloud.bigquery.v2.DatasetService.DeleteDataset"' --limit=5 --project=<PROJECT>.
   3. Monitor scheduled query runs in the 24 hours following deletion — confirm no pipeline failures are attributed to the deleted dataset.
   4. Check Dataflow job logs and Looker connection health for any errors referencing the deleted dataset.

   Response Shape

   1. Project and dataset identity confirmation
   2. Dataset inventory (tables, views, routines, bytes stored)
   3. Downstream dependency audit (scheduled queries, DTS jobs, authorized views)
   4. Export/backup confirmation before deletion
   5. Approval status
   6. Executed deletion or truncation action
   7. Post-change pipeline impact verification