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Database ⇄ Developer tools

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

Keep AWS Aurora MySQL 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 MySQL and Datadog

Keep AWS Aurora MySQL and Datadog in step: the rows in your database and the Incidents, Service Level Objectives, Hosts, Monitors your engineering tools track stay consistent in real time, in both directions.

AWS Aurora MySQL 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 Columns, Primary keys and indexes, Views, Foreign keys in AWS Aurora MySQL with Incidents, Service Level Objectives, Hosts, Monitors 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 Stream row changes from Aurora into SaaS tools via binlog CDC instead of scheduled batch exports.
  • 02 Sync a production Aurora cluster with an analytics database while filtering out sensitive columns.
  • 03 Pull the Hosts inventory into a CMDB or database for asset tracking, tag hygiene, and cost allocation across teams.
  • 04 Sync Monitors and their alert state into Postgres so reliability teams query alert history and noisy-monitor trends in SQL.

Common sync patterns

React to changes on either side in near real time

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

Where Datadog manages users or groups: keep identity aligned

Directory and identity records in Datadog stay matched to the users or owners table in AWS Aurora MySQL, so provisioning and de-provisioning flow from one source.

Turn rows into the records your tools track

A new or changed row in AWS Aurora MySQL creates or updates the matching record in Datadog, whether that is an issue, an event, a message, or a user, so the tool reflects the database without a custom API job.

What you can sync between AWS Aurora MySQL 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 MySQL objects Datadog objects How this pairing syncs
Views Can serve as read-only sync sources for derived or filtered datasets. 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. Views is specific to AWS Aurora MySQL and Events to Datadog — each maps to any object or custom field on the other side.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. 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. Foreign keys is specific to AWS Aurora MySQL and Dashboards to Datadog — each maps to any object or custom field on the other side.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. 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. Stored procedures and triggers is specific to AWS Aurora MySQL and Metrics to Datadog — each maps to any object or custom field on the other side.
Databases (schemas) Logical namespaces that scope which tables a sync connection can see. 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. Databases (schemas) is specific to AWS Aurora MySQL and Incidents to Datadog — each maps to any object or custom field on the other side.
Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. 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. Tables is specific to AWS Aurora MySQL and Service Level Objectives to Datadog — each maps to any object or custom field on the other side.
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. 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. Rows is specific to AWS Aurora MySQL and Hosts to Datadog — each maps to any object or custom field on the other side.

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

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.

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

Datadog AWS Aurora MySQL 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 MySQL 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 MySQL ⇄ Datadog

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

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

Real-time

Two-way sync

Changes in AWS Aurora MySQL 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 MySQL 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 MySQL or Datadog record.

Observability

Monitoring

Track your AWS Aurora MySQL ⇄ 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 MySQL and Datadog.

How the AWS Aurora MySQL and Datadog connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns 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 MySQL 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 MySQL 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 MySQL connected
    Datadog connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

AWS Aurora MySQL 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 329 integrations available for AWS Aurora MySQL and Datadog.

Popular · 8 of 329
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