n8n to Mode Integration
AI-first ETL from n8n into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads n8n into Mode
Datrise syncs n8n's workflows, executions, nodes, credentials, execution logs, and triggers 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
n8n: Workflow automation across apps and APIs.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How n8n entities map to Mode
| n8n entity | Mode object | Notes |
|---|---|---|
| workflows | n8n_workflows | id PK · custom fields → flattened columns for SQL and notebooks |
| executions | n8n_executions | id PK · linked to n8n_workflows |
| nodes | n8n_nodes | id PK · linked to n8n_workflows |
| credentials | n8n_credentials | id PK · linked to n8n_workflows |
FAQ
How does Datrise handle n8n'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 n8n to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for n8n
Early access
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