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

Keep Datadog and DuckDB 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 DuckDB

Keep DuckDB and Datadog in step: the rows in your database and the Service Level Objectives, Hosts, Monitors, Logs your engineering tools track stay consistent in real time, in both directions.

DuckDB 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 Attached databases, Database files, Schemas, Tables in DuckDB with Service Level Objectives, Hosts, Monitors, Logs 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 Push aggregates computed in DuckDB out to a CRM or business tools so analysis results reach operational systems.
  • 02 Use DuckDB as a transform step: read synced Parquet exports, aggregate with SQL, and write results back to an operational database.
  • 03 Sync Monitors and their alert state into Postgres so reliability teams query alert history and noisy-monitor trends in SQL.
  • 04 Provision and update Datadog Monitors from a config database or Git so alert definitions stay consistent across teams and environments.

Common sync patterns

Turn rows into the records your tools track

A new or changed row in DuckDB creates or updates the matching record in Datadog, whether that is an issue, an event, a message, or a user, so the tool reflects the database without a custom API job.

Land tool activity as queryable rows

Records and events from Datadog arrive in DuckDB 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 DuckDB and Stacksync keeps Datadog current, replacing the auth, webhooks, rate limits, and retry logic you would otherwise maintain for each tool.

What you can sync between Datadog and DuckDB

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 DuckDB 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. Attached databases Additional database files or external systems attached into one session for cross-source queries. Dashboards is specific to Datadog and Attached databases to DuckDB — 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. Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. Metrics is specific to Datadog and Database files to DuckDB — 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. Schemas Namespaces within a database used to organize tables in sync outputs. Incidents is specific to Datadog and Schemas to DuckDB — 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 Columnar tables created via SQL; the destination for materialized sync data. Service Level Objectives is specific to Datadog and Tables to DuckDB — 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 SQL views used to shape or filter data for downstream consumers. Hosts is specific to Datadog and Views to DuckDB — each maps to any object or custom field on the other side.
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. External files (Parquet/CSV/JSON) Files DuckDB queries in place without loading, common as a sync interchange format. Monitors is specific to Datadog and External files (Parquet/CSV/JSON) to DuckDB — each maps to any object or custom field on the other side.

How changes propagate between Datadog and DuckDB

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

DuckDB Datadog Interval-based propagation

DetectionStacksync polls DuckDB for changes on an incremental schedule, reading only records changed since the previous pass. Polling or full re-reads.

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.
  • DuckDB: No API rate limits; throughput is bounded by local compute and I/O.
What ships with Datadog ⇄ DuckDB

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Datadog or DuckDB 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 DuckDB record.

Observability

Monitoring

Track your Datadog ⇄ DuckDB 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 DuckDB.

How the Datadog and DuckDB 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.

DuckDB

Integration surface
In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
Authentication
None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
Change detection
Polling or full re-reads; no change feed or transaction log API
Capabilities
read · write
Rate limits
No API rate limits; throughput is bounded by local compute and I/O
How it works

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

    Choose tables

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

Datadog and DuckDB 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 327 integrations available for Datadog and DuckDB.

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