DatriseAI-first ETL

SparkPost to Looker Integration

AI-first ETL from SparkPost into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads SparkPost into Looker

Datrise syncs SparkPost's messages, transmissions, delivery events, bounces, suppressions, and sending domains into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

SparkPost: Transactional email delivery and deliverability metrics.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How SparkPost entities map to Looker

SparkPost entityLooker objectNotes
messagessparkpost_messagesid PK · custom fields → flattened columns (nested fields expanded for modeling)
transmissionssparkpost_transmissionsid PK · linked to sparkpost_messages
delivery eventssparkpost_delivery_eventsdate/time dimension columns events
bouncessparkpost_bouncesid PK · linked to sparkpost_messages

FAQ

How does Datrise handle SparkPost's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the SparkPost to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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