Amazon S3 to ThoughtSpot Integration
AI-first ETL from Amazon S3 into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into ThoughtSpot
Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes 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
Amazon S3: Object storage for files, exports, and data lakes.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Amazon S3 entities map to ThoughtSpot
| Amazon S3 entity | ThoughtSpot object | Notes |
|---|---|---|
| buckets | s3_buckets | id PK · custom fields → flattened columns for searchable fields |
| objects | s3_objects | id PK · linked to s3_buckets |
| prefixes | s3_prefixes | id PK · linked to s3_buckets |
| object metadata | s3_object_metadata | id PK · linked to s3_buckets |
FAQ
How does Datrise handle Amazon S3'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 Amazon S3 to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
Related pipelines
More destinations for Amazon S3
More sources for ThoughtSpot
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