DatriseAI-first ETL

Datadog to Amazon DynamoDB Integration

AI-first ETL from Datadog into Amazon DynamoDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Datadog into Amazon DynamoDB

Datrise syncs Datadog's metrics, monitors, events, logs, dashboards, and incidents into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

Datadog: Infrastructure monitoring, logs, and alerting.

Amazon DynamoDB: Serverless key-value and document store on AWS.

How Datadog entities map to Amazon DynamoDB

Datadog entityAmazon DynamoDB objectNotes
metricsdatadog_metricsid PK · custom fields → nested map/list attributes
monitorsdatadog_monitorsid PK · linked to datadog_metrics
eventsdatadog_eventsISO-8601 string or epoch number attributes events
logsdatadog_logsid PK · linked to datadog_metrics

FAQ

How does Datrise handle Datadog's custom fields in Amazon DynamoDB?

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

How does the Datadog to Amazon DynamoDB sync stay up to date?

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

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