Real-time sync
Changes in Azure OpenAI or StarRocks instantly reflect in both systems. No stale data, no manual imports.
Keep Azure OpenAI and StarRocks in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into StarRocks, so StarRocks always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.
StarRocks 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 StarRocks, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.
Rows added or changed in StarRocks flow into Azure OpenAI within seconds, so embeddings, classifications, and enrichments are computed on current data rather than a nightly extract.
Scores, labels, embeddings, or summaries produced in Azure OpenAI land in StarRocks as columns or tables, queryable and joinable with the rest of the business data.
As records change in StarRocks, matching Assistants, Vector stores, Deployments, Models in Azure OpenAI are inserted, updated, or removed, so what Azure OpenAI serves reflects the warehouse instead of a stale snapshot.
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 | StarRocks objects | How this pairing syncs | |
|---|---|---|---|
| Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. | Databases Top-level namespaces addressed exactly as in MySQL clients. | Usage and quota is specific to Azure OpenAI and Databases to StarRocks — 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 Defined with a table model (Primary Key, Unique Key, Aggregate, Duplicate Key) that determines update behavior. | Assistants is specific to Azure OpenAI and Tables to StarRocks — 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. | Materialized views Automatically maintained rollups used to accelerate queries on synced data. | Vector stores is specific to Azure OpenAI and Materialized views to StarRocks — 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. | Views Logical views for shaping analytical reads. | Deployments is specific to Azure OpenAI and Views to StarRocks — each maps to any object or custom field on the other side. | |
| Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. | Partitions Time or range partitions that scope loads and retention. | Models is specific to Azure OpenAI and Partitions to StarRocks — 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. | Columns Columnar storage with types mapped from source systems during sync. | Fine-tuning jobs is specific to Azure OpenAI and Columns to StarRocks — each maps to any object or custom field on the other side. |
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.
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 StarRocks as a row-level write, with types converted between the two schemas.
DetectionStacksync polls StarRocks for changes on an incremental schedule, reading only records changed since the previous pass. Query-based polling when reading.
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 StarRocks records.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–StarRocks connection.
Changes in Azure OpenAI or StarRocks instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Azure OpenAI or StarRocks data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Azure OpenAI or StarRocks record.
Track your Azure OpenAI ⇄ StarRocks sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Azure OpenAI and StarRocks.
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.
Authenticate Azure OpenAI and StarRocks with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Azure OpenAI and StarRocks 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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time integration between Azure OpenAI and StarRocks — Azure OpenAI is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.
Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Azure OpenAI and StarRocks records are not retained after a sync operation.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Azure OpenAI and StarRocks connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Azure OpenAI–StarRocks integration in-house.
Yes — Stacksync ships production-grade connectors for both Azure OpenAI and StarRocks. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Azure OpenAI: 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. On StarRocks: Query-based polling when reading; StarRocks is most often the destination side of a sync. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Azure OpenAI side: Assistants, Vector stores, Deployments, Models, plus custom fields where Azure OpenAI exposes them. On the StarRocks side: Tables, Materialized views, Views, Partitions. Stacksync auto-detects both schemas and converts types between the two systems.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 407 integrations available for Azure OpenAI and StarRocks.