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Database ⇄ Developer tools

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

Keep AWS Aurora PostgreSQL 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 Aurora PostgreSQL and Datadog

Keep AWS Aurora PostgreSQL and Datadog in step: the rows in your database and the Logs, Events, Dashboards, Metrics your engineering tools track stay consistent in real time, in both directions.

AWS Aurora PostgreSQL is where your application's durable data lives; Datadog is where engineering and operations teams track the issues, events, messages, or identities that run alongside it. The two overlap constantly, a row should open a ticket, an alert should land as a record, a user in one should exist in the other, but bridging them today means per-tool integration code: auth, webhooks, pagination, rate limits, and retries, built and maintained separately for every tool.

Stacksync syncs Tables, Rows, Columns, Primary keys and constraints in AWS Aurora PostgreSQL with Logs, Events, Dashboards, Metrics in Datadog field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync keeps every copy consistent and resolves conflicts by rules you set, so the database and the tooling around it never drift apart.

Common use cases

  • 01 Sync JSONB-heavy application data into structured objects in downstream business systems.
  • 02 Keep a customer-facing Aurora database aligned with an internal admin tool, with writes accepted on both sides.
  • 03 Provision and update Datadog Monitors from a config database or Git so alert definitions stay consistent across teams and environments.
  • 04 Publish deploy and pipeline Events, and submit custom Metrics, into Datadog from CI/CD or a data pipeline to enrich dashboards and correlation.

Common sync patterns

Land tool activity as queryable rows

Records and events from Datadog arrive in AWS Aurora PostgreSQL as rows, so tickets, alerts, messages, or identity changes become joinable data your services and reports read directly.

One integration pattern instead of per-tool API code

Read and write the synced tables in AWS Aurora PostgreSQL and Stacksync keeps Datadog current, replacing the auth, webhooks, rate limits, and retry logic you would otherwise maintain for each tool.

React to changes on either side in near real time

Updates in Datadog arrive as row changes in AWS Aurora PostgreSQL, and writes to AWS Aurora PostgreSQL propagate to Datadog within seconds, so triggers, jobs, and alerts fire without polling.

What you can sync between AWS Aurora PostgreSQL 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 Aurora PostgreSQL objects Datadog objects How this pairing syncs
Rows Inserted, updated, and deleted in both directions during bi-directional syncs. 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. Rows is specific to AWS Aurora PostgreSQL and Logs to Datadog — each maps to any object or custom field on the other side.
Columns Rich Postgres types including JSONB and arrays are mapped to the paired system's fields. 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. Columns is specific to AWS Aurora PostgreSQL and Events to Datadog — each maps to any object or custom field on the other side.
Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. 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. Primary keys and constraints is specific to AWS Aurora PostgreSQL and Dashboards to Datadog — each maps to any object or custom field on the other side.
Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. 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. Views and materialized views is specific to AWS Aurora PostgreSQL and Metrics to Datadog — each maps to any object or custom field on the other side.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Incidents Incident records from the v2 Incidents API with full CRUD, including status and timeline fields; landed in a database for MTTR reporting or created and updated from an external incident workflow. Foreign keys is specific to AWS Aurora PostgreSQL and Incidents to Datadog — each maps to any object or custom field on the other side.
Replication slots and publications The logical replication objects that power log-based CDC. Service Level Objectives SLO definitions and status history via the v1 SLO API with full CRUD; read out for reliability and error-budget reporting, or provisioned and updated from a reliability config. Replication slots and publications is specific to AWS Aurora PostgreSQL and Service Level Objectives to Datadog — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL 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 Aurora PostgreSQL Datadog Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

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

Datadog AWS Aurora PostgreSQL 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 applied to AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • 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 Aurora PostgreSQL ⇄ Datadog

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL 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 Aurora PostgreSQL or Datadog record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ 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 Aurora PostgreSQL and Datadog.

How the AWS Aurora PostgreSQL and Datadog connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC

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 Aurora PostgreSQL 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 Aurora PostgreSQL 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 Aurora PostgreSQL connected
    Datadog connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL 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 Aurora PostgreSQL ⇄ 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 Aurora PostgreSQL Datadog
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora PostgreSQL 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 331 integrations available for AWS Aurora PostgreSQL and Datadog.

Popular · 7 of 331
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