CSV File to ThoughtSpot Integration
AI-first ETL from CSV File into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into ThoughtSpot
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings 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
CSV File: Delimited flat files from exports and drops.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How CSV File entities map to ThoughtSpot
| CSV File entity | ThoughtSpot object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → flattened columns for searchable fields |
| columns | csv_file_columns | id PK · linked to csv_file_rows |
| headers | csv_file_headers | id PK · linked to csv_file_rows |
| delimiters | csv_file_delimiters | id PK · linked to csv_file_rows |
FAQ
How does Datrise handle CSV File'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 CSV File to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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