DatriseAI-first ETL

Teradata to ThoughtSpot Integration

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

How Datrise loads Teradata into ThoughtSpot

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history 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

Teradata: Teradata analytical database for enterprise workloads.

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

How Teradata entities map to ThoughtSpot

Teradata entityThoughtSpot objectNotes
tablesteradata_d_tablesid PK · custom fields → flattened columns for searchable fields
rowsteradata_d_rowsid PK · linked to teradata_d_tables
schemasteradata_d_schemasid PK · linked to teradata_d_tables
indexesteradata_d_indexesid PK · linked to teradata_d_tables

FAQ

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

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

Related pipelines

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