DatriseAI-first ETL

Sellsy to ThoughtSpot Integration

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

How Datrise loads Sellsy into ThoughtSpot

Datrise syncs Sellsy's prospects, opportunities, estimates, invoices, tickets, and timeline events 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

Sellsy: French CRM with quoting, invoicing, and support tickets.

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

How Sellsy entities map to ThoughtSpot

Sellsy entityThoughtSpot objectNotes
prospectssellsy_prospectsid PK · custom fields → flattened columns for searchable fields
opportunitiessellsy_opportunitiesid PK · linked to sellsy_prospects
estimatessellsy_estimatesid PK · linked to sellsy_prospects
invoicessellsy_invoicesid PK · linked to sellsy_prospects

FAQ

How does Datrise handle Sellsy'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 Sellsy to ThoughtSpot sync stay up to date?

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

Related pipelines

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