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Data warehouse ⇄ Developer tools

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

Keep Databricks 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Databricks and Datadog

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

Databricks is the central store where teams keep SQL Warehouses, Change Data Feed, Catalogs, Schemas 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 Dashboards, Metrics, Incidents, Service Level Objectives produced in Datadog are exactly what analysts want to measure in Databricks, and the curated rows in Databricks 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 SQL Warehouses, Change Data Feed, Catalogs, Schemas in Databricks with Dashboards, Metrics, Incidents, Service Level Objectives 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 CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 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

Warehouse signals reach Datadog

A row scored, flagged, or enriched in Databricks 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 Dashboards, Metrics, Incidents, Service Level Objectives into Databricks 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 Databricks 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.

Databricks objects Datadog objects How this pairing syncs
SQL Warehouses The compute endpoint a sync connects to for query execution. 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. SQL Warehouses is specific to Databricks and Hosts to Datadog — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. 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. Change Data Feed is specific to Databricks and Monitors to Datadog — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. 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. Catalogs is specific to Databricks and Logs to Datadog — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. 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. Schemas is specific to Databricks and Events to Datadog — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. 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. Delta Tables is specific to Databricks and Dashboards to Datadog — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. 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. Views is specific to Databricks and Metrics to Datadog — each maps to any object or custom field on the other side.

How changes propagate between Databricks 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.

Databricks Datadog Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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

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

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • 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 Databricks ⇄ Datadog

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks 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 Databricks or Datadog record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Databricks and Datadog connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

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

    Choose tables

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

Databricks 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
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ISO 27001
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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 429 integrations available for Databricks and Datadog.

Popular · 7 of 429
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