Skip to content
AI ⇄ Data warehouse

Azure OpenAI to Dremio integration — real-time data sync

Keep Azure OpenAI and Dremio in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Azure OpenAI and Dremio

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

Dremio 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 Dremio, 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 Land Fine-tuning jobs with their status, base model, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
  • 02 Poll Batch jobs into an operational database and fire the next pipeline step when a job's status turns to completed.
  • 03 Sync curated Dremio views into an operational Postgres so applications get low-latency access to lakehouse data.
  • 04 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.

Common sync patterns

Feed live warehouse records to Azure OpenAI

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

Model output back in the warehouse

Scores, labels, embeddings, or summaries produced in Azure OpenAI land in Dremio as columns or tables, queryable and joinable with the rest of the business data.

Keep an index in step with the source

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

What you can sync between Azure OpenAI and Dremio

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 Dremio objects How this pairing syncs
Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. Models is specific to Azure OpenAI and Sources to Dremio — each maps to any object or custom field on the other side.
Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. Fine-tuning jobs is specific to Azure OpenAI and Physical datasets to Dremio — each maps to any object or custom field on the other side.
Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. Files is specific to Azure OpenAI and Virtual datasets (views) to Dremio — 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. Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. Batch jobs is specific to Azure OpenAI and Apache Iceberg tables to Dremio — 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. Spaces and folders Namespaces that organize virtual datasets and govern access. Usage and quota is specific to Azure OpenAI and Spaces and folders to Dremio — 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. Reflections Materialized accelerations that make repeated extraction queries cheaper. Assistants is specific to Azure OpenAI and Reflections to Dremio — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Dremio

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

Dremio Azure OpenAI Interval-based propagation

DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.

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 Dremio 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.
  • Dremio: Bounded by engine capacity and workload management rather than API rate limits.
What ships with Azure OpenAI ⇄ Dremio

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Dremio 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 Dremio record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Dremio 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 Dremio.

How the Azure OpenAI and Dremio 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.

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits
How it works

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

    Choose tables

    Pick the Azure OpenAI and Dremio 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 ⇄ Dremio
    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 Dremio
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Azure OpenAI and Dremio 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 407 integrations available for Azure OpenAI and Dremio.

Popular · 6 of 407
All Azure OpenAI integrations All Dremio integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own