DataFactoryManagementClient

Name: DataFactoryManagementClient API
Brand: Data Factory Management
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAnalyticsData Pipelinesoauth276 EndpointsREST

For Agents

Provision Azure Data Factory factories and manage their pipelines, datasets, linked services, triggers, integration runtimes, and data flow debug sessions through 76 ARM operations.

Connect the DataFactoryManagementClient to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the DataFactoryManagementClient, 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 DataFactoryManagementClient API.

Create and configure Data Factory instances with optional source-control (Git) integration

Manage linked services that hold connection details for source and sink stores

Define datasets, pipelines, triggers, and data flows that compose ETL workflows

Provision and operate self-hosted and Azure-hosted integration runtimes for hybrid data movement

GET STARTED

Start building with DataFactoryManagementClient API

Explore with Jentic

View OpenAPI Document

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

Jentic publishes the only available OpenAPI specification for DataFactoryManagementClient, keeping it validated and agent-ready. The Azure Data Factory management API exposes 76 operations across 58 paths to provision factories and manage every artefact inside them: linked services, datasets, pipelines, triggers, integration runtimes, data flows, and debug sessions. Use it to stand up a new Data Factory, configure source-control integration, register linked services for source and sink stores, define and run pipelines, and operate self-hosted or Azure-hosted integration runtimes for hybrid ETL.

Use Cases

Patterns agents use DataFactoryManagementClient API for, with concrete tasks.

★ Provision a Data Factory with Git Integration

Create a Data Factory configured with a GitHub or Azure DevOps repository so all subsequent linked services, datasets, and pipelines are version-controlled. The Factories_CreateOrUpdate endpoint accepts a repoConfiguration block that points to the repo URL, branch, and root folder. Once configured, factory artefacts are saved to Git on edit and can be promoted across environments via standard pull request workflows.

Create a Data Factory named 'adf-prod' in resource group 'rg-data' with Azure DevOps repo configuration pointing to project 'data-platform' branch 'main'

Hybrid ETL with Self-Hosted Integration Runtimes

Provision a self-hosted integration runtime on a factory to move data from on-premises sources (SQL Server, file shares, network-isolated APIs) into Azure stores. The IntegrationRuntimes endpoints handle creation, key retrieval for installer registration, and status checks. Pair with linked services that reference the runtime so pipelines route their connections through the on-premises agent.

Create a self-hosted integration runtime named 'on-prem-ir' on factory 'adf-prod' and return its authentication keys for installer registration

Data Flow Debug Session Lifecycle

Start, attach, and stop data flow debug sessions to validate Mapping Data Flows during development. The DataFlowDebugSession endpoints create a session, add data flow definitions to it, and execute commands against the session. This is the same flow the Data Factory UI uses, so automation can build CI checks that exercise data flows before deployment.

Create a data flow debug session on factory 'adf-prod', add a Mapping Data Flow definition, and report the session ID and cluster status

Factory Inventory and Governance

List every Data Factory in a subscription to feed governance dashboards, validate that each factory has source control configured, and audit linked services for unsafe or missing connection-string secrets. The Factories_List, Factories_ListByResourceGroup, and per-factory linked services list endpoints together support this audit pattern with a few API calls.

List all Data Factories in subscription /subscriptions/{id}, then for each factory list its linked services and report any whose annotations are missing

AI Agent ETL Operations

An AI agent supporting a data engineering team can manage Data Factory through Jentic. The agent searches by intent ('create data factory linked service'), Jentic returns the LinkedServices_CreateOrUpdate operation with its schema, and the agent executes the call with managed Azure credentials. The same flow handles factory creation, integration runtime setup, and debug session lifecycle without manual portal steps.

Search Jentic for 'create an Azure Data Factory linked service', load the LinkedServices_CreateOrUpdate schema, and execute it with the user-supplied connectionString and runtime reference

Key Endpoints

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

METHOD

PATH

DESCRIPTION

GET

/subscriptions/{subscriptionId}/providers/Microsoft.DataFactory/factories

List all Data Factories in a subscription

DELETE

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}

Delete a Data Factory

POST

/subscriptions/{subscriptionId}/providers/Microsoft.DataFactory/locations/{locationId}/configureFactoryRepo

Configure factory repo settings at a location

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}/createDataFlowDebugSession

Create a data flow debug session

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}/addDataFlowToDebugSession

Add a data flow to a debug session

GET

/providers/Microsoft.DataFactory/operations

List Microsoft.DataFactory operations

GET

/subscriptions/{subscriptionId}/providers/Microsoft.DataFactory/factories

List all Data Factories in a subscription

DELETE

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}

Delete a Data Factory

POST

/subscriptions/{subscriptionId}/providers/Microsoft.DataFactory/locations/{locationId}/configureFactoryRepo

Configure factory repo settings at a location

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}/createDataFlowDebugSession

Create a data flow debug session

POST

/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.DataFactory/factories/{factoryName}/addDataFlowToDebugSession

Why though Jentic?

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

Credential isolation

Azure service principal credentials are encrypted in the Jentic vault. Agents receive scoped ARM bearer tokens at execution time and never see the underlying client secret. Linked-service connection strings remain server-side in the Data Factory and are not surfaced to the agent.

Intent-based discovery

Agents search by intent ('create data factory linked service' or 'list data factory triggers') and Jentic returns the matching operation from the 76 available, avoiding the need to load every endpoint.

Time to first call

Direct ARM integration: 2-3 days for AAD app registration, MSAL token handling, and the per-resource schema variety across factories, pipelines, and integration runtimes. Through Jentic: under 1 hour.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Databricks Management

Run heavy Spark and Delta Lake workloads from Data Factory pipelines.

Use alongside Data Factory when pipelines need to call Databricks notebook activities for large-scale transformations.

Complementary

Data Lake Store Account Management

Provision Data Lake storage that Data Factory pipelines read from and write to.

Use to set up the Data Lake store before configuring linked services in Data Factory.

Alternative

Stream Analytics Management

Continuous streaming analytics on Azure instead of batch ETL pipelines.

Choose Stream Analytics for real-time event processing; choose Data Factory for scheduled batch ETL workflows.

FAQs

Specific to using DataFactoryManagementClient API through Jentic.

Why is there no official OpenAPI spec for DataFactoryManagementClient?

Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call DataFactoryManagementClient via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Data Factory management API use?

It uses Azure Active Directory OAuth 2.0 against https://management.azure.com/ with the user_impersonation scope. The caller needs Data Factory Contributor or a custom role with Microsoft.DataFactory/* permissions. Jentic stores service principal credentials encrypted and issues short-lived bearer tokens at execution time.

Can I create linked services and pipelines through this API?

Yes. LinkedServices_CreateOrUpdate, Datasets_CreateOrUpdate, Pipelines_CreateOrUpdate, and Triggers_CreateOrUpdate are all in the spec — together they cover every artefact required to define an ETL workflow. Triggers must be started separately via Triggers_Start to begin execution.

What are the rate limits for the Data Factory management API?

Azure Resource Manager applies subscription-level read and write throttling (around 1,200 requests per hour). Data Factory additionally throttles concurrent pipeline runs based on the integration runtime's compute size; see IntegrationRuntime_GetStatus for current capacity.

How do I create a self-hosted integration runtime through Jentic?

Run pip install jentic and search for 'create data factory self-hosted integration runtime'. Jentic returns the IntegrationRuntimes_CreateOrUpdate operation; load its schema and execute it with kind 'SelfHosted'. Then call IntegrationRuntimes_ListAuthKeys to retrieve the key needed by the on-premises installer.

Does this API trigger pipeline runs?

Pipeline run creation is in the spec at the Pipelines/createRun and PipelineRuns endpoints. Use those to trigger a run or query historical runs. Trigger-based runs require Triggers_Start to enable the trigger first.