Datadog → Amazon Redshift
AI-first ETL from Datadog into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Datadog into Amazon Redshift
Datrise syncs Datadog's records, events, and configuration objects into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.
Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.
Ideal for AWS-native warehouses already using the Redshift ecosystem.
Endpoints
Datadog: SaaS or API data source for analytics and warehouse sync.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Datadog entities map to Amazon Redshift
| Datadog entity | Amazon Redshift object | Notes |
|---|---|---|
| records | datadog_records | id PK · custom fields → SUPER columns |
| events | datadog_events | TIMESTAMPTZ events |
| configuration objects | datadog_configuration_objects | id PK · linked to datadog_records |
FAQ
How does Datrise handle Datadog's custom fields in Amazon Redshift?
Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.
How does the Datadog to Amazon Redshift sync stay up to date?
It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.
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Early access
Connect Datadog to Amazon Redshift the easy way
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