DatriseAI-first ETL

Jira Cloud Google BigQuery

AI-first ETL from Jira Cloud into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Jira Cloud into Google BigQuery

Datrise syncs Jira Cloud's records, events, and configuration objects into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Jira Cloud: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Jira Cloud entities map to Google BigQuery

Jira Cloud entityGoogle BigQuery objectNotes
recordsjira_cloud_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsjira_cloud_eventsTIMESTAMP events
configuration objectsjira_cloud_configuration_objectsid PK · linked to jira_cloud_records

FAQ

How does Datrise handle Jira Cloud's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Jira Cloud to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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