Datadog to ThoughtSpot Integration
AI-first ETL from Datadog into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Datadog into ThoughtSpot
Datrise syncs Datadog's metrics, monitors, events, logs, dashboards, and incidents 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
Datadog: Infrastructure monitoring, logs, and alerting.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Datadog entities map to ThoughtSpot
| Datadog entity | ThoughtSpot object | Notes |
|---|---|---|
| metrics | datadog_metrics | id PK · custom fields → flattened columns for searchable fields |
| monitors | datadog_monitors | id PK · linked to datadog_metrics |
| events | datadog_events | date/time columns events |
| logs | datadog_logs | id PK · linked to datadog_metrics |
FAQ
How does Datrise handle Datadog'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 Datadog to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
Related pipelines
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