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Data warehouse ⇄ Developer tools

AWS S3 to Datadog integration — real-time, two-way sync

Keep AWS S3 and Datadog in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect AWS S3 and Datadog

Close the gap between analytics and operations: AWS S3 holds the record while Datadog runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

AWS S3 is the central store where teams keep Object Versions, Event Notifications, Access Points, Multipart Uploads for reporting and analysis; Datadog runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Monitors, Logs, Events, Dashboards produced in Datadog are exactly what analysts want to measure in AWS S3, and the curated rows in AWS S3 are what should drive the next action in Datadog. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Object Versions, Event Notifications, Access Points, Multipart Uploads in AWS S3 with Monitors, Logs, Events, Dashboards in Datadog field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Ingest partner or vendor file drops (CSV, JSON, Parquet) from a bucket into a database or CRM as records.
  • 02 Export synced operational data to S3 as files feeding a data lake or downstream batch jobs.
  • 03 Sync Monitors and their alert state into Postgres so reliability teams query alert history and noisy-monitor trends in SQL.
  • 04 Provision and update Datadog Monitors from a config database or Git so alert definitions stay consistent across teams and environments.

Common sync patterns

Operational data lands in AWS S3 for analytics

Records created in Datadog — issues, events, messages, metrics, or user changes — replicate into AWS S3 tables as they happen, so reporting runs on current data instead of last night's export.

Warehouse signals reach Datadog

A row scored, flagged, or enriched in AWS S3 creates or updates the matching record in Datadog, so the operational tool acts on the same data the analysts already see.

Backfill history, then stay live

Load the existing set of Monitors, Logs, Events, Dashboards into AWS S3 once, then keep it current with every change — you get full history plus real-time updates without a separate pipeline.

What you can sync between AWS S3 and Datadog

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

AWS S3 objects Datadog objects How this pairing syncs
Access Points Scoped network endpoints used to grant a sync narrow access to a bucket. Hosts Infrastructure host inventory with tags and metadata from the v1 host list API; loaded into a CMDB or warehouse for asset tracking, and hosts can be muted or unmuted via the API. Access Points is specific to AWS S3 and Hosts to Datadog — each maps to any object or custom field on the other side.
Multipart Uploads The mechanism used to write large export files reliably. Monitors Alert definitions with query, thresholds, and current state via the v1 Monitors API, which supports full create, update, and delete; Stacksync reads alert state into a warehouse or provisions and updates monitors from a config source. Multipart Uploads is specific to AWS S3 and Monitors to Datadog — each maps to any object or custom field on the other side.
Buckets Top-level containers a sync targets; region and policy are set at this level. Logs Log events searched via the v2 Logs search endpoint by time window and submittable through the log intake API; commonly streamed to a warehouse for retention beyond Datadog's storage period. Buckets is specific to AWS S3 and Logs to Datadog — each maps to any object or custom field on the other side.
Objects The stored files (CSV, JSON, Parquet); syncs read them as datasets or write exports into them. Events The event stream (deploys, alerts, comments) searched via the v2 Events endpoint and posted via POST /api/v1/events; used to correlate deploy and incident timelines or to publish deploy and pipeline events into Datadog. Objects is specific to AWS S3 and Events to Datadog — each maps to any object or custom field on the other side.
Prefixes Key-name paths used to partition synced datasets, since S3 has no real directories. Dashboards Dashboard definitions and widgets via the v1 Dashboards API with full CRUD; exported for backup and audit, or created and updated programmatically from a source of truth. Prefixes is specific to AWS S3 and Dashboards to Datadog — each maps to any object or custom field on the other side.
Object Metadata System and user-defined metadata read alongside object contents. Metrics Time-series metrics queried in aggregate windows through the query API and submitted via POST /api/v1/series; individual raw points cannot be extracted beyond retention. Object Metadata is specific to AWS S3 and Metrics to Datadog — each maps to any object or custom field on the other side.

How changes propagate between AWS S3 and Datadog

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

AWS S3 Datadog Sub-second propagation

DetectionAWS S3 notifies Stacksync of record changes through webhook events. S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge.

DeliveryEach detected change is written to Datadog through its API, with automatic retries and rate-limit backoff.

Datadog AWS S3 Sub-second propagation

DetectionDatadog notifies Stacksync of record changes through webhook events. Polling with time-windowed search queries on Logs and Events (timestamp cursor).

DeliveryEach detected change is written to AWS S3 through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • AWS S3: Request throughput scales per prefix; sustained high-volume workloads should spread keys across prefixes.
  • Datadog: Per-endpoint limits return HTTP 429 with X-RateLimit-Limit/-Remaining/-Period/-Reset headers. Log ingestion and metric submission are not rate limited; search endpoints such as Logs and Events queries carry quotas that Datadog Support can raise.
What ships with AWS S3 ⇄ Datadog

Connect AWS S3 and Datadog for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS S3–Datadog connection.

Real-time

Two-way sync

Changes in AWS S3 or Datadog instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS S3 or Datadog data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single AWS S3 or Datadog record.

Observability

Monitoring

Track your AWS S3 ⇄ Datadog sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS S3 and Datadog.

How the AWS S3 and Datadog connectors work

AWS S3

Integration surface
REST API (the S3 API), accessed directly or through AWS SDKs
Authentication
AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes
Change detection
S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge; list-based polling as a fallback
Capabilities
read · write · webhooks
Rate limits
Request throughput scales per prefix; sustained high-volume workloads should spread keys across prefixes

Datadog

Integration surface
REST API (v1 and v2)
Authentication
API key (DD-API-KEY) plus an Application key (DD-APPLICATION-KEY) sent as request headers; application keys are tied to the creating user and inherit that user's permissions and authorization scopes.
Change detection
Polling with time-windowed search queries on Logs and Events (timestamp cursor); monitor alerts can also push via the Webhooks notification integration. No modified-date CDC on mutable objects.
Capabilities
read · write · webhooks
Rate limits
Per-endpoint limits return HTTP 429 with X-RateLimit-Limit/-Remaining/-Period/-Reset headers. Log ingestion and metric submission are not rate limited; search endpoints such as Logs and Events queries carry quotas that Datadog Support can raise.
How it works

How to connect AWS S3 to Datadog — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate AWS S3 and Datadog with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    AWS S3 connected
    Datadog connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS S3 and Datadog objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · AWS S3 ⇄ Datadog
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    AWS S3 Datadog
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS S3 and Datadog integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 422 integrations available for AWS S3 and Datadog.

Popular · 8 of 422
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