DatriseAI-first ETL

Sendgrid Core Google BigQuery

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

How Datrise loads Sendgrid Core into Google BigQuery

Datrise syncs Sendgrid Core'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

Sendgrid Core: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Sendgrid Core entities map to Google BigQuery

Sendgrid Core entityGoogle BigQuery objectNotes
recordssendgrid_core_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventssendgrid_core_eventsTIMESTAMP events
configuration objectssendgrid_core_configuration_objectsid PK · linked to sendgrid_core_records

FAQ

How does Datrise handle Sendgrid Core'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 Core 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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