Real-time sync
Changes in Azure OpenAI or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.
Keep Azure OpenAI and Yellowbrick 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 Yellowbrick, so Yellowbrick always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.
Yellowbrick 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 Yellowbrick, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.
Combine Azure OpenAI's output with the tables already in Yellowbrick to report on model quality, cost, and coverage without exporting anything to a spreadsheet.
A continuously synced copy in Yellowbrick preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Azure OpenAI.
Rows added or changed in Yellowbrick flow into Azure OpenAI within seconds, so embeddings, classifications, and enrichments are computed on current data rather than a nightly extract.
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 | Yellowbrick 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. | Users and Roles Access-control objects that govern what a sync service account can read and write. | Models is specific to Azure OpenAI and Users and Roles to Yellowbrick — 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. | Databases Top-level containers for schemas and tables. | Fine-tuning jobs is specific to Azure OpenAI and Databases to Yellowbrick — 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. | Schemas Namespaces used to organize synced datasets by source or domain. | Files is specific to Azure OpenAI and Schemas to Yellowbrick — 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. | Tables Columnar MPP tables; the primary targets for warehouse syncs. | Batch jobs is specific to Azure OpenAI and Tables to Yellowbrick — 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. | Views Logical views used to shape reads for BI and downstream syncs. | Usage and quota is specific to Azure OpenAI and Views to Yellowbrick — 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 Yellowbrick as a row-level write, with types converted between the two schemas.
DetectionStacksync polls Yellowbrick for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp columns.
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 Yellowbrick records.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–Yellowbrick connection.
Changes in Azure OpenAI or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Azure OpenAI or Yellowbrick 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 Yellowbrick record.
Track your Azure OpenAI ⇄ Yellowbrick sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Azure OpenAI and Yellowbrick.
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 Yellowbrick 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 Yellowbrick 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 Yellowbrick — 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.
On the Azure OpenAI side: Models, Fine-tuning jobs, Files, Batch jobs, plus custom fields where Azure OpenAI exposes them. On the Yellowbrick side: Tables, Views, Users and Roles, Databases. Stacksync auto-detects both schemas and converts types between the two systems.
Azure OpenAI is a read-only source, so this integration runs one-way: Stacksync reads from Azure OpenAI in real time and delivers into Yellowbrick. Field mapping and monitoring work the same as for two-way pairs.
Common patterns for Azure OpenAI and Yellowbrick: One place to analyze AI results; History that outlives a run; Feed live warehouse records to Azure OpenAI. Combine Azure OpenAI's output with the tables already in Yellowbrick to report on model quality, cost, and coverage without exporting anything to a spreadsheet.
Azure OpenAI: 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. Yellowbrick: SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility. Authentication: Database credentials, with LDAP and Kerberos options in enterprise deployments. Stacksync manages authentication, retries, and rate limits on both sides.
Azure OpenAI: Deployments, quota, and resource creation live on the Azure Resource Manager control plane (Microsoft.CognitiveServices), separate from the data-plane inference API. Yellowbrick: The front end is PostgreSQL-compatible, so standard Postgres drivers and SQL tooling connect without custom clients. Stacksync's field mapping accounts for these differences between Azure OpenAI and Yellowbrick without custom code.
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 404 integrations available for Azure OpenAI and Yellowbrick.