Mailchimp → Mode
AI-first ETL from Mailchimp into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mailchimp into Mode
Datrise syncs Mailchimp's audiences, campaigns, automations, subscribers, and engagement metrics 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
Mailchimp: Email marketing platform with audiences and campaign analytics.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Mailchimp entities map to Mode
| Mailchimp entity | Mode object | Notes |
|---|---|---|
| audiences | mailchimp_audiences | id PK · custom fields → flattened columns for SQL and notebooks |
| campaigns | mailchimp_campaigns | id PK · linked to mailchimp_audiences |
| automations | mailchimp_automations | id PK · linked to mailchimp_audiences |
| subscribers | mailchimp_subscribers | id PK · linked to mailchimp_audiences |
FAQ
How does Datrise handle Mailchimp'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 Mailchimp to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
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Early access
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