Real-time
Two-way sync
Changes in Datadog or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Developer tools ⇄ Database
Datadog to Postgres Heroku integration — real-time, two-way sync
Keep Datadog and Postgres Heroku in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Datadog and Postgres Heroku
Keep Postgres Heroku and Datadog in step: the rows in your database and the Hosts, Monitors, Logs, Events your engineering tools track stay consistent in real time, in both directions.
Postgres Heroku 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, Views, Materialized Views, Schemas in Postgres Heroku with Hosts, Monitors, Logs, Events 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 Expose CRM objects as Postgres tables the Heroku application can query and join directly
- 02 Sync Heroku Postgres into a warehouse for reporting without running ETL dynos
- 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 Postgres Heroku 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 Postgres Heroku 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 Postgres Heroku, and writes to Postgres Heroku propagate to Datadog within seconds, so triggers, jobs, and alerts fire without polling.
What you can sync between Datadog and Postgres Heroku
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.
| Datadog objects | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| 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. | Materialized Views Precomputed result sets synced outward on refresh. | Incidents is specific to Datadog and Materialized Views to Postgres Heroku — each maps to any object or custom field on the other side. | |
| 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. | Schemas Namespaces that scope which tables a sync reads and writes. | Service Level Objectives is specific to Datadog and Schemas to Postgres Heroku — each maps to any object or custom field on the other side. | |
| 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. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Hosts is specific to Datadog and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. | |
| 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. | JSONB Columns Semi-structured payloads for nested SaaS objects and metadata. | Monitors is specific to Datadog and JSONB Columns to Postgres Heroku — each maps to any object or custom field on the other side. | |
| 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. | Sequences Generate surrogate keys for rows created by inbound syncs. | Logs is specific to Datadog and Sequences to Postgres Heroku — each maps to any object or custom field on the other side. | |
| 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. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | Events is specific to Datadog and Follower Databases to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Datadog and Postgres Heroku
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.
Datadog Postgres Heroku 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 Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Datadog Interval-based propagation
DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.
DeliveryEach detected change is written to Datadog through its API, with automatic retries and rate-limit backoff.
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.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Datadog ⇄ Postgres Heroku
Connect Datadog and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Datadog–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Datadog or Postgres Heroku 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 Datadog or Postgres Heroku record.
Observability
Monitoring
Track your Datadog ⇄ Postgres Heroku sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Datadog and Postgres Heroku.
How the Datadog and Postgres Heroku connectors work
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.
Postgres Heroku
- Integration surface
- SQL wire protocol (standard PostgreSQL)
- Authentication
- Database credentials from the Heroku DATABASE_URL config var; SSL required
- Change detection
- Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
- Capabilities
- read · write
- Rate limits
- No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works
How to connect Datadog to Postgres Heroku — 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.
- 01
Connect your apps
Authenticate Datadog and Postgres Heroku with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Datadog
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Datadog and Postgres Heroku 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.
objects · Datadog ⇄ Postgres HerokuCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Datadog Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Datadog and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Datadog and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Datadog's Incidents and Service Level Objectives), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
On the Postgres Heroku side: Tables, Views, Materialized Views, Schemas, plus custom fields where Postgres Heroku exposes them. On the Datadog side: Hosts, Monitors, Logs, Events. Stacksync auto-detects both schemas and converts types between the two systems.
-
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
-
Common patterns for Datadog and Postgres Heroku: Land tool activity as queryable rows; One integration pattern instead of per-tool API code; React to changes on either side in near real time. Records and events from Datadog arrive in Postgres Heroku as rows, so tickets, alerts, messages, or identity changes become joinable data your services and reports read directly.
-
Datadog: 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. Postgres Heroku: SQL wire protocol (standard PostgreSQL). Authentication: Database credentials from the Heroku DATABASE_URL config var; SSL required. Stacksync manages authentication, retries, and rate limits on both sides.
-
Postgres Heroku: Credentials are managed by Heroku through the DATABASE_URL config var and can rotate, so integrations should tolerate credential changes. Datadog: Datadog is multi-region (US1, US3, US5, EU1, AP1, US1-FED); the API host differs per site (api.datadoghq.com vs api.datadoghq.eu) and API and application keys are scoped to a single site. Stacksync's field mapping accounts for these differences between Datadog and Postgres Heroku without custom code.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 327 integrations available for Datadog and Postgres Heroku.
Popular · 8 of 327
-
Datadog ⇄ Google Cloud Spanner - Datadog ⇄ Google Cloud SQL
-
Datadog ⇄ IBM AS/400 -
Datadog ⇄ IBM Informix -
Postgres Heroku ⇄ Jms -
Postgres Heroku ⇄ Newrelic -
Postgres Heroku ⇄ Opsgenie -
Postgres Heroku ⇄ PagerDuty
No integrations match .
