SparkPost to Mode Integration

How Datrise loads SparkPost into Mode

Datrise syncs SparkPost's messages, transmissions, delivery events, bounces, suppressions, and sending domains into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

SparkPost: Transactional email delivery and deliverability metrics.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How SparkPost entities map to Mode

SparkPost entity	Mode object	Notes
messages	`sparkpost_messages`	id PK · custom fields → flattened columns for SQL and notebooks
transmissions	`sparkpost_transmissions`	id PK · linked to sparkpost_messages
delivery events	`sparkpost_delivery_events`	temporal columns events
bounces	`sparkpost_bounces`	id PK · linked to sparkpost_messages

FAQ

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

Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.

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

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

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