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Database ⇄ AI

Apache Cassandra to Azure OpenAI integration — real-time data sync

Keep Apache Cassandra 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 Apache Cassandra and Azure OpenAI

Sync the records in Apache Cassandra 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 Apache Cassandra, so Apache Cassandra 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. Apache Cassandra 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 Apache Cassandra it describes are two halves of the same thing, and they drift the moment one is updated without the other.

Stacksync syncs Secondary Indexes, User-Defined Types, Collections, Counters in Apache Cassandra with Models, Fine-tuning jobs, Files, Batch jobs in Azure OpenAI in real time. Rows created or changed in Apache Cassandra 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 Apache Cassandra, 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 Apache Cassandra 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 Sync the Deployments inventory (model, version, TPM capacity) into Postgres so platform teams track every Azure OpenAI deployment across subscriptions in SQL.
  • 02 Land Fine-tuning jobs with their status, base model, and result Files in a warehouse to power MLOps dashboards without per-viewer API calls.
  • 03 Replicate high-volume event or profile tables from Cassandra into a warehouse for analytics that CQL cannot express.
  • 04 Sync customer profile data between Cassandra and a CRM so operational apps and sales tools agree.

Common sync patterns

Run the AI on current data

Rows created or changed in Apache Cassandra 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 Apache Cassandra, next to the source data your applications already query.

Keep derived data fresh as sources change

When a row in Apache Cassandra is updated or removed, its counterpart in Azure OpenAI is updated or removed too, so nothing in Azure OpenAI describes a record that has since changed or gone.

What you can sync between Apache Cassandra 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.

Apache Cassandra objects Azure OpenAI objects How this pairing syncs
Counters Increment-only counter columns, usually read-only in syncs. Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Counters is specific to Apache Cassandra and Vector stores to Azure OpenAI — each maps to any object or custom field on the other side.
Keyspaces Top-level namespaces with replication settings that scope a sync connection. Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Keyspaces is specific to Apache Cassandra and Deployments to Azure OpenAI — each maps to any object or custom field on the other side.
Tables Wide-column tables addressed by partition key, the unit of row-level sync. Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Tables is specific to Apache Cassandra and Models to Azure OpenAI — each maps to any object or custom field on the other side.
Partitions and Rows Records located by partition and clustering keys during reads and upserts. Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Partitions and Rows is specific to Apache Cassandra and Fine-tuning jobs to Azure OpenAI — each maps to any object or custom field on the other side.
Materialized Views Server-maintained denormalized views; considered experimental and disabled by default in recent releases. Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Materialized Views is specific to Apache Cassandra and Files to Azure OpenAI — each maps to any object or custom field on the other side.
Secondary Indexes Optional indexes that allow filtered reads outside the partition key. Batch jobs Asynchronous bulk-inference jobs; status and output-file IDs are polled to completion to drive downstream pipeline triggers. Secondary Indexes is specific to Apache Cassandra and Batch jobs to Azure OpenAI — each maps to any object or custom field on the other side.

How changes propagate between Apache Cassandra 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.

Apache Cassandra Azure OpenAI Sub-second propagation

DetectionChanges in Apache Cassandra are captured at the source via change data capture — no polling loop against its API. Commit-log based CDC on tables with CDC enabled, or polling using writetime metadata and 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 Apache Cassandra records.

Azure OpenAI Apache Cassandra 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 written to Apache Cassandra through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Apache Cassandra: No API quotas; throughput is governed by cluster capacity and consistency-level choices.
  • 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 Apache Cassandra ⇄ Azure OpenAI

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Cassandra 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 Apache Cassandra or Azure OpenAI record.

Observability

Monitoring

Track your Apache Cassandra ⇄ 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 Apache Cassandra and Azure OpenAI.

How the Apache Cassandra and Azure OpenAI connectors work

Apache Cassandra

Integration surface
CQL over the Cassandra native binary protocol
Authentication
Database credentials (password authenticator); TLS and role-based grants where configured
Change detection
Commit-log based CDC on tables with CDC enabled, or polling using writetime metadata and timestamp columns
Capabilities
read · write · CDC
Rate limits
No API quotas; throughput is governed by cluster capacity and consistency-level choices

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 Apache Cassandra 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 Apache Cassandra 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
    Apache Cassandra connected
    Azure OpenAI connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Apache Cassandra and Azure OpenAI integration FAQ

SECURITY

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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 403 integrations available for Apache Cassandra and Azure OpenAI.

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