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Data warehouse ⇄ Developer tools

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

Keep BigQuery 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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Why teams connect BigQuery and Datadog

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

BigQuery is the central store where teams keep Projects, Tables, Partitioned tables, Clustered 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 Incidents, Service Level Objectives, Hosts, Monitors produced in Datadog are exactly what analysts want to measure in BigQuery, and the curated rows in BigQuery 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 Projects, Tables, Partitioned tables, Clustered tables in BigQuery 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 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 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 02 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 03 Load Events and Incidents into a warehouse to correlate deploys, alerts, and outages on one timeline for post-incident review.
  • 04 Pull the Hosts inventory into a CMDB or database for asset tracking, tag hygiene, and cost allocation across teams.

Common sync patterns

Backfill history, then stay live

Load the existing set of Incidents, Service Level Objectives, Hosts, Monitors into BigQuery 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.

One shared record, kept consistent

Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.

What you can sync between BigQuery 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.

BigQuery objects Datadog objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. 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. Tables is specific to BigQuery and Metrics to Datadog — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. 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. Partitioned tables is specific to BigQuery and Incidents to Datadog — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. 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. Clustered tables is specific to BigQuery and Service Level Objectives to Datadog — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. 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. Datasets is specific to BigQuery and Hosts to Datadog — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. 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. Projects is specific to BigQuery and Monitors to Datadog — each maps to any object or custom field on the other side.

How changes propagate between BigQuery 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.

BigQuery Datadog Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

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

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

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • 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 BigQuery ⇄ Datadog

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery 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 BigQuery or Datadog record.

Observability

Monitoring

Track your BigQuery ⇄ Datadog sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Datadog.

How the BigQuery and Datadog connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

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

    Choose tables

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

BigQuery 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 421 integrations available for BigQuery and Datadog.

Popular · 7 of 421
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