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Developer tools ⇄ Data warehouse

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

Keep Datadog and Yellowbrick 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 Yellowbrick

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

Yellowbrick is the central store where teams keep Users and Roles, Databases, Schemas, 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 Yellowbrick, and the curated rows in Yellowbrick 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 Users and Roles, Databases, Schemas, Tables in Yellowbrick 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 Land ERP transactional extracts in Yellowbrick for finance and supply-chain analytics.
  • 02 Push warehouse-computed aggregates or segments back into operational tools such as a CRM.
  • 03 Publish deploy and pipeline Events, and submit custom Metrics, into Datadog from CI/CD or a data pipeline to enrich dashboards and correlation.
  • 04 Load Events and Incidents into a warehouse to correlate deploys, alerts, and outages on one timeline for post-incident review.

Common sync patterns

Backfill history, then stay live

Load the existing set of Incidents, Service Level Objectives, Hosts, Monitors into Yellowbrick 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 Datadog and Yellowbrick

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 Yellowbrick objects How this pairing syncs
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. Schemas Namespaces used to organize synced datasets by source or domain. Dashboards is specific to Datadog and Schemas to Yellowbrick — 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. Tables Columnar MPP tables; the primary targets for warehouse syncs. Metrics is specific to Datadog and Tables to Yellowbrick — 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. Views Logical views used to shape reads for BI and downstream syncs. Incidents is specific to Datadog and Views to Yellowbrick — 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. Users and Roles Access-control objects that govern what a sync service account can read and write. Service Level Objectives is specific to Datadog and Users and Roles to Yellowbrick — 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. Databases Top-level containers for schemas and tables. Hosts is specific to Datadog and Databases to Yellowbrick — each maps to any object or custom field on the other side.

How changes propagate between Datadog and Yellowbrick

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

Yellowbrick Datadog Interval-based propagation

DetectionStacksync polls Yellowbrick for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp columns.

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.
  • Yellowbrick: No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
What ships with Datadog ⇄ Yellowbrick

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Datadog or Yellowbrick 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 Yellowbrick record.

Observability

Monitoring

Track your Datadog ⇄ Yellowbrick 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 Yellowbrick.

How the Datadog and Yellowbrick 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.

Yellowbrick

Integration surface
SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility
Authentication
Database credentials, with LDAP and Kerberos options in enterprise deployments
Change detection
Polling on timestamp columns; no exposed transaction-log CDC
Capabilities
read · write
Rate limits
No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
How it works

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

    Choose tables

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

Datadog and Yellowbrick 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 310 integrations available for Datadog and Yellowbrick.

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