DatriseAI-first ETL

SendGrid to Google BigQuery Integration

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

How Datrise loads SendGrid into Google BigQuery

Datrise syncs SendGrid's messages, contacts, delivery events, bounces, opens, and marketing campaigns 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

SendGrid: Transactional email delivery and engagement events.

Google BigQuery: Serverless analytics warehouse on GCP.

How SendGrid entities map to Google BigQuery

SendGrid entityGoogle BigQuery objectNotes
messagessendgrid_core_messagesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
contactssendgrid_core_contactsid PK · linked to sendgrid_core_messages
delivery eventssendgrid_core_delivery_eventsTIMESTAMP events
bouncessendgrid_core_bouncesid PK · linked to sendgrid_core_messages

FAQ

How does Datrise handle SendGrid'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 SendGrid 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

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