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Datadog to TimescaleDB integration — real-time, two-way sync

Keep Datadog and TimescaleDB 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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Why teams connect Datadog and TimescaleDB

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

TimescaleDB 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 Schemas, Hypertables, Chunks, Continuous Aggregates in TimescaleDB 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 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 Replicate subscription and billing events from operational Postgres tables into Timescale hypertables for time-series analysis.
  • 02 Keep device or asset reference tables bi-directionally in sync between TimescaleDB and an ERP.
  • 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

Land tool activity as queryable rows

Records and events from Datadog arrive in TimescaleDB 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 TimescaleDB 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 TimescaleDB, and writes to TimescaleDB propagate to Datadog within seconds, so triggers, jobs, and alerts fire without polling.

What you can sync between Datadog and TimescaleDB

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 TimescaleDB objects How this pairing syncs
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. Schemas Postgres namespaces used to separate synced datasets by team or environment. Events is specific to Datadog and Schemas to TimescaleDB — each maps to any object or custom field on the other side.
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. Hypertables Time-partitioned tables that hold the main time-series data; the primary read and write target in syncs. Dashboards is specific to Datadog and Hypertables to TimescaleDB — each maps to any object or custom field on the other side.
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. Chunks Time-bounded partitions of a hypertable; syncs read and write through the parent hypertable and never address chunks directly. Metrics is specific to Datadog and Chunks to TimescaleDB — each maps to any object or custom field on the other side.
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. Continuous Aggregates Incrementally maintained rollups that serve as pre-aggregated read sources for downstream systems. Incidents is specific to Datadog and Continuous Aggregates to TimescaleDB — 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. Regular PostgreSQL Tables Relational reference data such as devices, tenants, or accounts synced alongside the series data. Service Level Objectives is specific to Datadog and Regular PostgreSQL Tables to TimescaleDB — 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. Views Standard SQL views used to shape or filter data for consumers. Hosts is specific to Datadog and Views to TimescaleDB — each maps to any object or custom field on the other side.

How changes propagate between Datadog and TimescaleDB

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 TimescaleDB 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 TimescaleDB as a row-level write, with types converted between the two schemas.

TimescaleDB Datadog Sub-second propagation

DetectionChanges in TimescaleDB are captured at the source via change data capture — no polling loop against its API. Log-based capture via PostgreSQL logical decoding where the deployment allows it — hypertable changes surface on the underlying chunk tables and must.

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.
  • TimescaleDB: No API rate limits; throughput is bounded by database resources and connection limits.
What ships with Datadog ⇄ TimescaleDB

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Datadog or TimescaleDB 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 TimescaleDB record.

Observability

Monitoring

Track your Datadog ⇄ TimescaleDB 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 TimescaleDB.

How the Datadog and TimescaleDB 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.

TimescaleDB

Integration surface
SQL wire protocol (PostgreSQL)
Authentication
Database credentials
Change detection
Log-based capture via PostgreSQL logical decoding where the deployment allows it — hypertable changes surface on the underlying chunk tables and must be remapped to the parent — or timestamp-based polling on time columns; regular Postgres tables replicate through standard logical replication
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by database resources and connection limits.
How it works

How to connect Datadog to TimescaleDB — 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 Datadog and TimescaleDB 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
    Datadog connected
    TimescaleDB connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Datadog and TimescaleDB 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 · Datadog ⇄ TimescaleDB
    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
    Datadog TimescaleDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Datadog and TimescaleDB 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 308 integrations available for Datadog and TimescaleDB.

Popular · 7 of 308
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