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Data warehouse ⇄ Developer tools

Apache Pinot to Datadog integration — real-time, two-way sync

Keep Apache Pinot 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 Apache Pinot and Datadog

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

Apache Pinot is the central store where teams keep Real-time Tables, Offline Tables, Indexes, Tenants 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 Apache Pinot, and the curated rows in Apache Pinot 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 Real-time Tables, Offline Tables, Indexes, Tenants in Apache Pinot 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 Serve user-facing analytics from Pinot while syncing daily rollups to finance and ops tools.
  • 02 Keep upsert-enabled real-time tables aligned with mutable operational records streamed from source systems.
  • 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 Apache Pinot — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in Apache Pinot for analytics

Records created in Datadog — issues, events, messages, metrics, or user changes — replicate into Apache Pinot 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 Apache Pinot 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 Apache Pinot 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.

Apache Pinot objects Datadog objects How this pairing syncs
Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. 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. Tables is specific to Apache Pinot and Dashboards to Datadog — each maps to any object or custom field on the other side.
Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. 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. Schemas is specific to Apache Pinot and Metrics to Datadog — each maps to any object or custom field on the other side.
Segments Immutable data files that batch ingestion uploads and the cluster serves. 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. Segments is specific to Apache Pinot and Incidents to Datadog — each maps to any object or custom field on the other side.
Real-time Tables Tables fed continuously from streams like Kafka, including upsert-enabled tables. 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. Real-time Tables is specific to Apache Pinot and Service Level Objectives to Datadog — each maps to any object or custom field on the other side.
Offline Tables Batch-loaded tables merged with real-time data at query time. 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. Offline Tables is specific to Apache Pinot and Hosts to Datadog — each maps to any object or custom field on the other side.
Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. 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. Indexes is specific to Apache Pinot and Monitors to Datadog — each maps to any object or custom field on the other side.

How changes propagate between Apache Pinot 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.

Apache Pinot Datadog Interval-based propagation

DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.

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

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

Rate-limit considerations

  • Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
  • 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 Apache Pinot ⇄ Datadog

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Pinot 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 Apache Pinot or Datadog record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Apache Pinot and Datadog connectors work

Apache Pinot

Integration surface
REST API (SQL queries via the broker; administration via the controller); JDBC client available
Authentication
Deployment-dependent: HTTP basic authentication or token-based auth where enabled
Change detection
Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
Capabilities
read · write
Rate limits
No fixed API quotas; query throughput depends on broker and server sizing

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

    Choose tables

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

Apache Pinot 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.

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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 316 integrations available for Apache Pinot and Datadog.

Popular · 7 of 316
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