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Data warehouse ⇄ AI

Apache Druid to Azure OpenAI integration — real-time data sync

Keep Apache Druid and Azure OpenAI 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 Druid and Azure OpenAI

Flow Azure OpenAI data into Apache Druid in real time — no exports, no schedulers, no custom scripts.

Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into Apache Druid, so Apache Druid always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.

Apache Druid holds the raw records the business runs on; Azure OpenAI turns those records into embeddings, scores, labels, and summaries. The two meet wherever a warehouse row needs to be enriched by a model and the result needs somewhere durable to live. Most teams stitch that meeting together with export scripts and a queue, then spend their time keeping the glue alive.

The payoff is that model output stops living in a separate place from the data it describes. Once results sit in Apache Druid, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.

Common use cases

  • 01 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 02 Land Fine-tuning jobs with their status, base model, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
  • 03 Query aggregated event metrics from Druid and sync them into CRM account fields for usage-based selling.
  • 04 Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.

Common sync patterns

Keep an index in step with the source

As records change in Apache Druid, matching Models, Fine-tuning jobs, Files, Batch jobs in Azure OpenAI are inserted, updated, or removed, so what Azure OpenAI serves reflects the warehouse instead of a stale snapshot.

One place to analyze AI results

Combine Azure OpenAI's output with the tables already in Apache Druid to report on model quality, cost, and coverage without exporting anything to a spreadsheet.

History that outlives a run

A continuously synced copy in Apache Druid preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Azure OpenAI.

What you can sync between Apache Druid and Azure OpenAI

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 Druid objects Azure OpenAI objects How this pairing syncs
Dimensions String and categorical columns used for filtering and grouping in synced queries. Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Dimensions is specific to Apache Druid and Usage and quota to Azure OpenAI — each maps to any object or custom field on the other side.
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Metrics is specific to Apache Druid and Assistants to Azure OpenAI — each maps to any object or custom field on the other side.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Ingestion Supervisors is specific to Apache Druid and Vector stores to Azure OpenAI — each maps to any object or custom field on the other side.
Lookups Key-value mappings joined at query time, refreshable from external systems. Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Lookups is specific to Apache Druid and Deployments to Azure OpenAI — each maps to any object or custom field on the other side.
Tasks Batch ingestion and compaction jobs monitored during data loads. Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Tasks is specific to Apache Druid and Models to Azure OpenAI — each maps to any object or custom field on the other side.
Datasources The table-like unit of storage and querying, the main target of reads and ingestion. Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Datasources is specific to Apache Druid and Fine-tuning jobs to Azure OpenAI — each maps to any object or custom field on the other side.

How changes propagate between Apache Druid and Azure OpenAI

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 Druid Azure OpenAI Interval-based propagation

DetectionStacksync polls Apache Druid for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Druid through streaming or batch ingestion rather than row updates.

DeliveryAzure OpenAI does not accept inbound record writes, so this direction carries requests rather than records: Azure OpenAI's output flows back as field updates on the originating Apache Druid records.

Azure OpenAI Apache Druid Interval-based propagation

DetectionStacksync polls Azure OpenAI for changes on an incremental schedule, reading only records changed since the previous pass. Polling: list endpoints plus GET on job IDs for status.

DeliveryEach detected change is applied to Apache Druid as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Druid: No fixed API quotas; query concurrency is bounded by broker and historical node capacity.
  • Azure OpenAI: Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
What ships with Apache Druid ⇄ Azure OpenAI

Connect Apache Druid and Azure OpenAI for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Azure OpenAI connection.

Real-time

Real-time sync

Changes in Apache Druid or Azure OpenAI instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or Azure OpenAI 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 Druid or Azure OpenAI record.

Observability

Monitoring

Track your Apache Druid ⇄ Azure OpenAI sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Druid and Azure OpenAI.

How the Apache Druid and Azure OpenAI connectors work

Apache Druid

Integration surface
REST API (SQL over HTTP and native JSON queries); JDBC via Avatica
Authentication
Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy
Change detection
Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
Capabilities
read · write
Rate limits
No fixed API quotas; query concurrency is bounded by broker and historical node capacity

Azure OpenAI

Integration surface
REST data-plane (inference + authoring) and Azure Resource Manager control-plane
Authentication
API key in the api-key header, or a Microsoft Entra ID bearer token / managed identity
Change detection
Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states.
Capabilities
read
Rate limits
Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
How it works

How to connect Apache Druid to Azure OpenAI — 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 Druid and Azure OpenAI 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 Druid connected
    Azure OpenAI connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and Azure OpenAI 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 Druid ⇄ Azure OpenAI
    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 Druid Azure OpenAI
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and Azure OpenAI integration FAQ

SECURITY

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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 413 integrations available for Apache Druid and Azure OpenAI.

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