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Developer tools ⇄ Data warehouse

Datadog to Google Cloud Platform integration — real-time, two-way sync

Keep Datadog and Google Cloud Platform 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 Datadog and Google Cloud Platform

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

Google Cloud Platform is the central store where teams keep Firestore documents, Spanner tables, BigQuery datasets, BigQuery tables 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 Hosts, Monitors, Logs, Events produced in Datadog are exactly what analysts want to measure in Google Cloud Platform, and the curated rows in Google Cloud Platform 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 Firestore documents, Spanner tables, BigQuery datasets, BigQuery tables in Google Cloud Platform 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 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 Publish change events to Pub/Sub so downstream services react to record updates as they happen.
  • 02 Sync Monitors and their alert state into Postgres so reliability teams query alert history and noisy-monitor trends in SQL.
  • 03 Provision and update Datadog Monitors from a config database or Git so alert definitions stay consistent across teams and environments.

Common sync patterns

Warehouse signals reach Datadog

A row scored, flagged, or enriched in Google Cloud Platform 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 Hosts, Monitors, Logs, Events into Google Cloud Platform once, then keep it current with every change — you get full history plus real-time updates without a separate pipeline.

No batch jobs to babysit

New and changed records move field by field the moment they change, replacing scheduled ETL and one-off scripts that fail quietly and leave stale rows behind.

What you can sync between Datadog and Google Cloud Platform

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 Google Cloud Platform objects How this pairing 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. Pub/Sub topics Event streams used to move change events between systems in near real time. Logs is specific to Datadog and Pub/Sub topics to Google Cloud Platform — 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. Firestore documents Document data read and written through the Firestore API for app-facing syncs. Events is specific to Datadog and Firestore documents to Google Cloud Platform — 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. Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. Dashboards is specific to Datadog and Spanner tables to Google Cloud Platform — 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. BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. Metrics is specific to Datadog and BigQuery datasets to Google Cloud Platform — 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. BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL. Incidents is specific to Datadog and BigQuery tables to Google Cloud Platform — 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. Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. Service Level Objectives is specific to Datadog and Cloud SQL databases to Google Cloud Platform — each maps to any object or custom field on the other side.

How changes propagate between Datadog and Google Cloud Platform

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 Google Cloud Platform 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 Google Cloud Platform as a row-level write, with types converted between the two schemas.

Google Cloud Platform Datadog Sub-second propagation

DetectionGoogle Cloud Platform pushes changes as they happen — webhook events backed by change data capture. Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery.

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.
  • Google Cloud Platform: Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits.
What ships with Datadog ⇄ Google Cloud Platform

Connect Datadog and Google Cloud Platform for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Datadog or Google Cloud Platform 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 Google Cloud Platform record.

Observability

Monitoring

Track your Datadog ⇄ Google Cloud Platform 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 Google Cloud Platform.

How the Datadog and Google Cloud Platform 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.

Google Cloud Platform

Integration surface
Per-service REST and gRPC APIs; BigQuery speaks SQL and Cloud SQL exposes standard database wire protocols
Authentication
IAM service accounts with OAuth 2.0 tokens
Change detection
Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery tables
Capabilities
read · write · CDC · webhooks
Rate limits
Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits
How it works

How to connect Datadog to Google Cloud Platform — 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 Google Cloud Platform 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
    Google Cloud Platform connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Datadog and Google Cloud Platform 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 ⇄ Google Cloud Platform
    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 Google Cloud Platform
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Datadog and Google Cloud Platform 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 418 integrations available for Datadog and Google Cloud Platform.

Popular · 7 of 418
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