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Developer tools ⇄ Data warehouse

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

Keep Datadog and Firebolt 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 Firebolt

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

Firebolt is the central store where teams keep Views, Aggregating indexes, Engines, Databases 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 Firebolt, and the curated rows in Firebolt 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 Views, Aggregating indexes, Engines, Databases in Firebolt 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 Keep dimension tables aligned with source systems while high-volume event data loads through separate batch pipelines.
  • 02 Sync aggregated results from Firebolt back to operational tools that need computed metrics (reverse ETL).
  • 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

Keep user and access records aligned

Where Datadog manages users, directory, or access data, those records stay current in Firebolt — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in Firebolt for analytics

Records created in Datadog — issues, events, messages, metrics, or user changes — replicate into Firebolt tables as they happen, so reporting runs on current data instead of last night's export.

Warehouse signals reach Datadog

A row scored, flagged, or enriched in Firebolt creates or updates the matching record in Datadog, so the operational tool acts on the same data the analysts already see.

What you can sync between Datadog and Firebolt

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 Firebolt 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. External tables References to files in object storage used to stage bulk loads. Logs is specific to Datadog and External tables to Firebolt — 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. Views Curated query surfaces commonly used as sources for reverse ETL. Events is specific to Datadog and Views to Firebolt — 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. Aggregating indexes Precomputed rollups maintained at write time; incremental loads update them automatically. Dashboards is specific to Datadog and Aggregating indexes to Firebolt — 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. Engines Compute resources that must be running for a sync to read or write. Metrics is specific to Datadog and Engines to Firebolt — 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. Databases Logical containers holding the tables a sync targets. Incidents is specific to Datadog and Databases to Firebolt — 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. Tables Managed columnar tables written with SQL; the main sync destination. Service Level Objectives is specific to Datadog and Tables to Firebolt — each maps to any object or custom field on the other side.

How changes propagate between Datadog and Firebolt

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

Firebolt Datadog Interval-based propagation

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

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.
  • Firebolt: No fixed request quota; throughput depends on the engine size attached to the workload.
What ships with Datadog ⇄ Firebolt

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Datadog or Firebolt 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 Firebolt record.

Observability

Monitoring

Track your Datadog ⇄ Firebolt 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 Firebolt.

How the Datadog and Firebolt 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.

Firebolt

Integration surface
SQL over a REST API, with JDBC, Python, and Node.js SDKs
Authentication
Service account credentials (client ID and secret) exchanged for OAuth 2.0 tokens
Change detection
Polling; Firebolt is an analytics destination and does not expose a change feed
Capabilities
read · write
Rate limits
No fixed request quota; throughput depends on the engine size attached to the workload
How it works

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

    Choose tables

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

Datadog and Firebolt 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 320 integrations available for Datadog and Firebolt.

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