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Database ⇄ AI

Amazon Aurora to Azure OpenAI integration — real-time data sync

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

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Why teams connect Amazon Aurora and Azure OpenAI

Sync the records in Amazon Aurora into Azure OpenAI and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.

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

AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. Amazon Aurora is where those source records actually live. The bridge between the two is the row itself, since an item in Azure OpenAI and the record in Amazon Aurora it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Views, Materialized Views, Columns and Data Types, Primary and Foreign Keys in Amazon Aurora with Vector stores, Deployments, Models, Fine-tuning jobs in Azure OpenAI in real time. Rows created or changed in Amazon Aurora flow into Azure OpenAI so inference and embedding run on current data, and the scores, labels, and generated fields Azure OpenAI produces flow back onto the matching rows in Amazon Aurora, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.

Because matching is by a stable identifier, every row in Amazon Aurora stays tied to its AI-side counterpart in Azure OpenAI. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.

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 Offload sync reads to Aurora reader endpoints to avoid load on the writer instance.
  • 04 Two-way sync between Aurora application tables and a CRM so product data and account data stay consistent.

Common sync patterns

One record, one identifier

Each item in Azure OpenAI carries the key of the row in Amazon Aurora it came from, so results resolve back to the exact record with nothing orphaned or duplicated.

Run the AI on current data

Rows created or changed in Amazon Aurora flow into Azure OpenAI as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.

Write results back onto the record

Scores, labels, extracted fields, or generated text produced in Azure OpenAI land on the matching row in Amazon Aurora, next to the source data your applications already query.

What you can sync between Amazon Aurora 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.

Amazon Aurora objects Azure OpenAI objects How this pairing syncs
Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Materialized Views is specific to Amazon Aurora and Fine-tuning jobs to Azure OpenAI — each maps to any object or custom field on the other side.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Columns and Data Types is specific to Amazon Aurora and Files to Azure OpenAI — each maps to any object or custom field on the other side.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. Batch jobs Asynchronous bulk-inference jobs; status and output-file IDs are polled to completion to drive downstream pipeline triggers. Primary and Foreign Keys is specific to Amazon Aurora and Batch jobs to Azure OpenAI — each maps to any object or custom field on the other side.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Read Replicas is specific to Amazon Aurora and Usage and quota to Azure OpenAI — each maps to any object or custom field on the other side.
Databases Logical databases within a cluster that scope a sync connection. Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Databases is specific to Amazon Aurora and Assistants to Azure OpenAI — each maps to any object or custom field on the other side.
Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Schemas is specific to Amazon Aurora and Vector stores to Azure OpenAI — each maps to any object or custom field on the other side.

How changes propagate between Amazon Aurora 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.

Amazon Aurora Azure OpenAI Sub-second propagation

DetectionChanges in Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.

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 Amazon Aurora records.

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

Rate-limit considerations

  • Amazon Aurora: No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits.
  • 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 Amazon Aurora ⇄ Azure OpenAI

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or Azure OpenAI record.

Observability

Monitoring

Track your Amazon Aurora ⇄ 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 Amazon Aurora and Azure OpenAI.

How the Amazon Aurora and Azure OpenAI connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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 Amazon Aurora 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 Amazon Aurora 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
    Amazon Aurora connected
    Azure OpenAI connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon Aurora 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 · Amazon Aurora ⇄ 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
    Amazon Aurora Azure OpenAI
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon Aurora and Azure OpenAI 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 407 integrations available for Amazon Aurora and Azure OpenAI.

Popular · 7 of 407
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