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AI ⇄ Data warehouse

Azure OpenAI to Starburst Enterprise integration — real-time data sync

Keep Azure OpenAI and Starburst Enterprise 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 Azure OpenAI and Starburst Enterprise

Flow Azure OpenAI data into Starburst Enterprise 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 Starburst Enterprise, so Starburst Enterprise always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.

Starburst Enterprise 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 Starburst Enterprise, 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 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
  • 02 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 03 Consolidate reads from multiple databases behind a single connection instead of maintaining one integration per source
  • 04 Write curated or reconciled results back to lakehouse tables through connectors that support inserts

Common sync patterns

One place to analyze AI results

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

History that outlives a run

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

Feed live warehouse records to Azure OpenAI

Rows added or changed in Starburst Enterprise flow into Azure OpenAI within seconds, so embeddings, classifications, and enrichments are computed on current data rather than a nightly extract.

What you can sync between Azure OpenAI and Starburst Enterprise

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.

Azure OpenAI objects Starburst Enterprise objects How this pairing syncs
Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Columns Typed per the Trino type system, mapped from each source's native types. Files is specific to Azure OpenAI and Columns to Starburst Enterprise — each maps to any object or custom field on the other side.
Batch jobs Asynchronous bulk-inference jobs; status and output-file IDs are polled to completion to drive downstream pipeline triggers. Catalogs Each catalog maps to a connector (Iceberg, Hive, PostgreSQL, and others) exposing an external source. Batch jobs is specific to Azure OpenAI and Catalogs to Starburst Enterprise — each maps to any object or custom field on the other side.
Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Schemas Namespaces within a catalog, mirroring the underlying source's databases or schemas. Usage and quota is specific to Azure OpenAI and Schemas to Starburst Enterprise — each maps to any object or custom field on the other side.
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Tables Queryable relations; writes pass through to sources whose connectors support them. Assistants is specific to Azure OpenAI and Tables to Starburst Enterprise — each maps to any object or custom field on the other side.
Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Views Engine-level SQL views used to shape federated data before syncing it out. Vector stores is specific to Azure OpenAI and Views to Starburst Enterprise — each maps to any object or custom field on the other side.
Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Materialized views Precomputed results that make repeated sync reads cheaper. Deployments is specific to Azure OpenAI and Materialized views to Starburst Enterprise — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Starburst Enterprise

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.

Azure OpenAI Starburst Enterprise 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 Starburst Enterprise as a row-level write, with types converted between the two schemas.

Starburst Enterprise Azure OpenAI Interval-based propagation

DetectionStacksync polls Starburst Enterprise for changes on an incremental schedule, reading only records changed since the previous pass. Query-based polling.

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 Starburst Enterprise records.

Rate-limit considerations

  • 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.
  • Starburst Enterprise: Throughput is governed by cluster sizing and resource groups rather than API quotas.
What ships with Azure OpenAI ⇄ Starburst Enterprise

Connect Azure OpenAI and Starburst Enterprise for flexible, real-time data sync.

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Starburst Enterprise 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 Azure OpenAI or Starburst Enterprise record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Azure OpenAI and Starburst Enterprise.

How the Azure OpenAI and Starburst Enterprise connectors work

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.

Starburst Enterprise

Integration surface
ANSI SQL over JDBC/ODBC drivers and the Trino client REST protocol
Authentication
Deployment-dependent: username/password, LDAP, OAuth 2.0, or Kerberos
Change detection
Query-based polling; Starburst is a query engine and exposes no change log of its own
Capabilities
read · write
Rate limits
Throughput is governed by cluster sizing and resource groups rather than API quotas
How it works

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

    Choose tables

    Pick the Azure OpenAI and Starburst Enterprise 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 · Azure OpenAI ⇄ Starburst Enterprise
    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
    Azure OpenAI Starburst Enterprise
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Starburst Enterprise 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 409 integrations available for Azure OpenAI and Starburst Enterprise.

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