Skip to content
Data warehouse ⇄ AI

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

Keep Apache Pinot 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.

  • 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 Apache Pinot and Azure OpenAI

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

Apache Pinot 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 Apache Pinot, 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 Poll Batch jobs into an operational database and fire the next pipeline step when a job's status turns to completed.
  • 02 Pull per-deployment TPM/RPM usage into a warehouse for FinOps chargeback and quota-exhaustion alerting.
  • 03 Serve user-facing analytics from Pinot while syncing daily rollups to finance and ops tools.
  • 04 Keep upsert-enabled real-time tables aligned with mutable operational records streamed from source systems.

Common sync patterns

History that outlives a run

A continuously synced copy in Apache Pinot preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Azure OpenAI.

Feed live warehouse records to Azure OpenAI

Rows added or changed in Apache Pinot 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 Apache Pinot as columns or tables, queryable and joinable with the rest of the business data.

What you can sync between Apache Pinot 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 Pinot objects Azure OpenAI objects How this pairing syncs
Offline Tables Batch-loaded tables merged with real-time data at query time. Usage and quota Per-deployment TPM/RPM consumption and remaining quota, read from usage endpoints and Azure Monitor for cost and throttling reporting. Offline Tables is specific to Apache Pinot and Usage and quota to Azure OpenAI — each maps to any object or custom field on the other side.
Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Indexes is specific to Apache Pinot and Assistants to Azure OpenAI — each maps to any object or custom field on the other side.
Tenants Logical groupings that isolate workloads on shared clusters. Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Tenants is specific to Apache Pinot and Vector stores to Azure OpenAI — each maps to any object or custom field on the other side.
Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Tables is specific to Apache Pinot and Deployments to Azure OpenAI — each maps to any object or custom field on the other side.
Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Schemas is specific to Apache Pinot and Models to Azure OpenAI — each maps to any object or custom field on the other side.
Segments Immutable data files that batch ingestion uploads and the cluster serves. Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Segments is specific to Apache Pinot and Fine-tuning jobs to Azure OpenAI — each maps to any object or custom field on the other side.

How changes propagate between Apache Pinot 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 Pinot Azure OpenAI Interval-based propagation

DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.

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 Pinot records.

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

Rate-limit considerations

  • Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
  • 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 Pinot ⇄ Azure OpenAI

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

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

Real-time

Real-time sync

Changes in Apache Pinot 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 Pinot 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 Pinot or Azure OpenAI record.

Observability

Monitoring

Track your Apache Pinot ⇄ 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 Pinot and Azure OpenAI.

How the Apache Pinot and Azure OpenAI connectors work

Apache Pinot

Integration surface
REST API (SQL queries via the broker; administration via the controller); JDBC client available
Authentication
Deployment-dependent: HTTP basic authentication or token-based auth where enabled
Change detection
Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
Capabilities
read · write
Rate limits
No fixed API quotas; query throughput depends on broker and server sizing

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

    Choose tables

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

Apache Pinot 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.

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 410 integrations available for Apache Pinot and Azure OpenAI.

Popular · 7 of 410
All Apache Pinot integrations All Azure OpenAI 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