StreamAnalyticsManagementClient

★ Only Publicly Available OpenAPI DocumentAnalyticsData Pipelinesoauth27 EndpointsREST

For Agents

Manage Stream Analytics user-defined functions on a streaming job: create, update, list, test, and retrieve default definitions.

Connect the StreamAnalyticsManagementClient to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the StreamAnalyticsManagementClient, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with StreamAnalyticsManagementClient API.

Create or update a user-defined function on a Stream Analytics streaming job

List every function defined on a given streaming job

Retrieve the default definition for an Azure ML function binding

Test a function with a sample input to verify behaviour before deployment

GET STARTED

Start building with StreamAnalyticsManagementClient API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for StreamAnalyticsManagementClient, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for StreamAnalyticsManagementClient, keeping it validated and agent-ready. The Azure Stream Analytics management API exposes functions defined inside a streaming job — user-defined functions (UDFs), Azure Machine Learning bindings, and JavaScript scalar functions — together with operations to test them and retrieve default definitions. It covers create, update, list, delete, test, and retrieve-default-definition for functions on a streaming job. Use it to manage the function layer of a Stream Analytics job from CI/CD or operational scripts.

Use Cases

Patterns agents use StreamAnalyticsManagementClient API for, with concrete tasks.

★ CI/CD for Stream Analytics Functions

Stream Analytics queries often depend on JavaScript UDFs and Azure ML scoring bindings that need to evolve alongside the streaming job's logic. StreamAnalyticsManagementClient supports create-or-update, patch, and delete on functions per streaming job, so a deployment pipeline can apply function changes from a Git repository without going through the Azure portal. The test endpoint lets the pipeline validate behaviour before promoting the change.

Create a JavaScript UDF named 'parseSensorPayload' on streaming job 'iot-pipeline' in resource group 'iot-rg' with the supplied function body, then run the test endpoint with a sample payload.

Wiring Azure ML Models into Streaming Jobs

Stream Analytics can call Azure ML web services as scalar functions. The retrieve-default-definition endpoint introspects the ML web service and returns the function definition shape (inputs, outputs, binding); the create-or-update endpoint then registers it on the streaming job. This is how a real-time scoring pipeline goes from a trained model to live inference inside a SQL-like streaming query.

Call the retrieve-default-definition endpoint for ML web service URL https://example.azureml.net/score, then create a function 'scoreFraud' on streaming job 'fraud-pipeline' using the returned definition.

Pre-Deployment Function Testing

Before pushing a function change to a running streaming job, teams want to verify the function returns the expected output for a representative input. The /test endpoint accepts a function and a sample payload and returns the result, letting a pipeline gate promotion on a successful test. This eliminates a class of late-night incidents caused by broken UDF logic.

POST to the test endpoint for function 'parseSensorPayload' on streaming job 'iot-pipeline' with the sample payload {"raw":"42,77"}.

Agent-Driven Streaming Pipeline Updates via Jentic

An AI agent supporting a data engineering team can propose and apply changes to Stream Analytics functions through Jentic. The agent searches by intent, loads the create-or-update operation schema, and submits the updated function body. Azure AD tokens stay inside the Jentic vault, so the agent never holds credentials directly.

Use Jentic to load the StreamAnalyticsManagementClient create-or-update function operation, then update function 'parseSensorPayload' with a new JavaScript body.

Key Endpoints

7 endpoints — jentic publishes the only available openapi specification for streamanalyticsmanagementclient, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

GET

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions

List functions on a streaming job

PUT

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Create or update a function

PATCH

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Patch a function

GET

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Get a function by name

DELETE

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Delete a function

POST

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}/RetrieveDefaultDefinition

Retrieve default definition for an Azure ML function binding

POST

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}/test

Test a function with a sample payload

GET

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions

List functions on a streaming job

PUT

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Create or update a function

PATCH

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Patch a function

GET

/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}

Get a function by name

DELETE

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Azure AD OAuth tokens are stored encrypted in the Jentic vault (MAXsystem). Agents calling StreamAnalyticsManagementClient receive scoped, short-lived bearer tokens at execution time — refresh tokens and tenant secrets never enter the agent's context.

Intent-based discovery

Agents search Jentic by intent (e.g., 'add an azure ml function to stream analytics') and Jentic returns the matching operation with its full input schema, so the agent can call create, update, or test without reading ARM docs.

Time to first call

Direct integration with Azure Resource Manager and the Stream Analytics function model — JS UDF wrapping, ML binding shapes, MSAL token handling — typically takes a full day. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

SqlManagementClient

Inspect Azure SQL Database schemas often used as a Stream Analytics output sink

Pair when validating that a Stream Analytics job's SQL output sink schema matches the expected columns.

Complementary

StorageImportExport

Bulk transfer of historical data into Azure Storage that Stream Analytics can later reference

Useful when seeding reference data into blob storage before connecting Stream Analytics.

Alternative

Cloud Dataflow

Google Cloud's managed streaming analytics service

Choose when the streaming pipeline runs on Google Cloud rather than Azure.

FAQs

Specific to using StreamAnalyticsManagementClient API through Jentic.

Why is there no official OpenAPI spec for StreamAnalyticsManagementClient?

Microsoft Azure publishes Swagger fragments for the Microsoft.StreamAnalytics resource provider but does not publish a consolidated, validated OpenAPI 3 spec for the functions surface. Jentic generates and maintains this spec so that AI agents and developers can call StreamAnalyticsManagementClient via structured tooling. It is validated against the live Azure Resource Manager API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the StreamAnalyticsManagementClient use?

All endpoints require Azure Active Directory OAuth 2.0, declared as the azure_auth security scheme with the user_impersonation scope against https://login.microsoftonline.com. Through Jentic, Azure tokens are held in the vault and injected at call time so the agent never sees the raw bearer token.

Can I test a function before deploying it?

Yes. POST /subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.StreamAnalytics/streamingjobs/{jobName}/functions/{functionName}/test runs the function against a supplied sample payload and returns the result, so a pipeline can validate behaviour before promoting the change.

What are the rate limits for the StreamAnalyticsManagementClient?

Calls go through Azure Resource Manager, which throttles writes per subscription (typically 1,200 writes per hour) and reads more generously. Function create-or-update is synchronous on the management plane; the streaming job picks up new function definitions on the next start.

How do I bind an Azure ML model as a function through Jentic?

Run pip install jentic, search Jentic for 'add an azure ml function to stream analytics', call retrieve-default-definition with the ML web service URL, then load the create-or-update function operation and submit the returned definition. Jentic handles the Azure AD token exchange.

Does this API manage Stream Analytics inputs and outputs?

No. This spec covers only functions on a streaming job (UDFs and ML bindings). Inputs, outputs, transformation queries, and the streaming job lifecycle live under separate Microsoft.StreamAnalytics endpoints not included here.