PipeRun to Mode Integration
AI-first ETL from PipeRun into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads PipeRun into Mode
Datrise syncs PipeRun's deals, contacts, companies, pipeline stages, activities, and sales teams 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
PipeRun: Sales pipeline management for Brazilian revenue teams.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How PipeRun entities map to Mode
| PipeRun entity | Mode object | Notes |
|---|---|---|
| deals | piperun_deals | id PK · custom fields → flattened columns for SQL and notebooks |
| contacts | piperun_contacts | id PK · linked to piperun_deals |
| companies | piperun_companies | id PK · linked to piperun_deals |
| pipeline stages | piperun_pipeline_stages | id PK · linked to piperun_deals |
FAQ
How does Datrise handle PipeRun'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 PipeRun to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for PipeRun
Early access
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