DatriseAI-first ETL

PipeRun to ThoughtSpot Integration

AI-first ETL from PipeRun into ThoughtSpot. Governed entities, incremental sync, typed landing tables.

How Datrise loads PipeRun into ThoughtSpot

Datrise syncs PipeRun's deals, contacts, companies, pipeline stages, activities, and sales teams into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.

Ideal for natural-language search analytics over a warehouse.

Endpoints

PipeRun: Sales pipeline management for Brazilian revenue teams.

ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.

How PipeRun entities map to ThoughtSpot

PipeRun entityThoughtSpot objectNotes
dealspiperun_dealsid PK · custom fields → flattened columns for searchable fields
contactspiperun_contactsid PK · linked to piperun_deals
companiespiperun_companiesid PK · linked to piperun_deals
pipeline stagespiperun_pipeline_stagesid PK · linked to piperun_deals

FAQ

How does Datrise handle PipeRun's custom fields in ThoughtSpot?

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

How does the PipeRun to ThoughtSpot sync stay up to date?

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

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