Overview

Azure Data Factory (ADF) is a managed cloud service designed for data integration and transformation, specializing in orchestrating analytical workflows. It provides a serverless environment for constructing complex extract, transform, and load (ETL) and extract, load, and transform (ELT) pipelines, enabling data movement and processing across various data sources and sinks. ADF supports over 100 connectors for both on-premises and cloud-based data stores, including databases, file systems, SaaS applications, and big data platforms Azure Data Factory connector overview. This extensive connectivity facilitates hybrid data integration, allowing enterprises to unify data from disparate systems into a centralized analytics platform, such as Azure Synapse Analytics or Azure Data Lake Storage.

ADF is particularly suited for organizations needing to migrate existing SQL Server Integration Services (SSIS) packages to the cloud, as it offers an SSIS Integration Runtime (IR) that natively executes SSIS workloads Azure SSIS Integration Runtime explanation. This capability enables a lift-and-shift migration strategy, preserving investments in on-premises ETL solutions while leveraging Azure's scalability and managed service benefits. Beyond migration, ADF's visual development environment, Mapping Data Flows, enables data engineers to build data transformation logic without writing code, leveraging a graphical interface to design and execute data transformations at scale Azure Data Factory Mapping Data Flows concepts. This low-code approach can accelerate development cycles for common data preparation tasks.

The service also integrates with other Azure services, such as Azure Functions, Azure Databricks, and Azure Machine Learning, to extend its data processing capabilities. For instance, ADF can orchestrate the execution of Databricks notebooks for complex Spark transformations or trigger Azure Functions for custom logic. Its control flow features, including conditional logic, loops, and error handling, support the creation of robust and resilient data pipelines. The monitoring and alerting features provide visibility into pipeline execution status and performance, aiding in troubleshooting and operational management. For organizations managing large volumes of data and intricate integration requirements, ADF offers a scalable and managed solution for data orchestration.

Key features

  • Data Orchestration: Create, schedule, and manage complex data workflows (pipelines) that automate data movement and transformation across diverse data sources.
  • Hybrid Data Integration: Connects to over 100 data stores, both on-premises and in the cloud, facilitating centralized data management and analytics Azure Data Factory supported connectors.
  • SSIS Integration Runtime: Provides a managed service environment to execute existing SQL Server Integration Services (SSIS) packages in Azure SSIS Integration Runtime overview.
  • Mapping Data Flows: A visual, low-code transformation tool that allows users to design and execute data transformations without manual coding Mapping Data Flows documentation.
  • Code-Free Development: Offers a web-based user interface (Azure Data Factory Studio) for designing and monitoring data pipelines.
  • Monitoring and Management: Provides built-in tools for monitoring pipeline runs, activities, and alerts, offering insights into operational performance.
  • Extensibility: Integrates with other Azure services like Azure Functions, Azure Databricks, and Azure Machine Learning for advanced processing and analytics tasks.
  • Pipeline Debugging and Testing: Features interactive debugging to test transformations and orchestrations before deployment.

Pricing

Azure Data Factory operates on a pay-as-you-go model, with costs determined by the specific components and resources consumed. Pricing is granular and varies based on factors such as the number of data pipeline orchestration runs, data movement activity duration, Data Flow execution duration and compute size, and the uptime of the SSIS Integration Runtime. There is a free grant for the first 50,000 data pipeline orchestration runs per month and 10 activity runs for the SSIS Integration Runtime. Detailed pricing information is available on the official Azure pricing page.

Azure Data Factory Pricing Summary (As of 2026-05-07)
Component Pricing Model Details
Data Pipeline Orchestration Per run Charged per activity run within a pipeline. First 50,000 runs/month are free.
Data Movement Activities Per Data Integration Unit (DIU) hour Costs based on DIU duration, which reflects data transfer and processing capacity.
Mapping Data Flows Per vCore hour Charged based on the compute (vCore) and duration of the data flow execution.
SSIS Integration Runtime (IR) Per node hour Based on the type of node (Standard/Premium) and the uptime of the IR. Free tier for 10 activity runs/month.
External Pipeline Activities Per run Triggering external services like Azure Databricks or Azure Functions incurs a per-run cost.

For current pricing details, refer to the official Azure Data Factory pricing page.

Common integrations

Alternatives

  • AWS Glue: A serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development.
  • Google Cloud Dataflow: A fully managed service for executing Apache Beam pipelines that perform data analytics, ETL, and real-time computation.
  • Talend: An open-source data integration platform that provides a suite of tools for ETL, data quality, and data governance, available both on-premises and in the cloud. Talend offers robust data preparation capabilities, as described by InfoQ's review of Talend Data Fabric.
  • AWS Data Pipeline: A web service that helps you reliably process and transform data at specified intervals using a variety of AWS compute and storage services.
  • Google Cloud Dataproc: A managed Spark and Hadoop service that enables users to run open-source data tools for batch processing, querying, streaming, and machine learning.

Getting started

To get started with Azure Data Factory using the Python SDK, you first need to authenticate and create a Data Factory client. The following example demonstrates how to create a simple pipeline that copies data from a blob storage to another blob storage (assuming the necessary linked services and datasets are already configured in ADF).

Prerequisites:

  • Azure account with an active subscription.
  • Azure Data Factory instance created.
  • Azure Blob Storage accounts (source and sink) with containers.
  • Python installed, with azure-mgmt-datafactory, azure-identity, and msrestazure packages.

from azure.identity import DefaultAzureCredential
from azure.mgmt.datafactory import DataFactoryManagementClient

# --- Configuration --- #
subscription_id = "YOUR_SUBSCRIPTION_ID"
resource_group_name = "YOUR_RESOURCE_GROUP_NAME"
data_factory_name = "YOUR_DATA_FACTORY_NAME"

# --- Authenticate --- #
# Use DefaultAzureCredential for a straightforward authentication experience in various environments
# (e.g., VS Code, Azure CLI, environment variables)
credential = DefaultAzureCredential()

# --- Create Data Factory Management Client --- #
data_factory_client = DataFactoryManagementClient(credential, subscription_id)

# --- Define Pipeline --- #
# This example assumes you have 'SourceBlobStorageLinkedService',
# 'SinkBlobStorageLinkedService', 'SourceDataset', and 'SinkDataset'
# already created in your Azure Data Factory.
# You would typically define these via the UI or ARM templates.

pipeline_name = "CopyBlobDataPipeline"

pipeline_definition = {
    "activities": [
        {
            "name": "CopyDataFromBlob",
            "type": "Copy",
            "inputs": [
                {
                    "referenceName": "SourceDataset",
                    "type": "DatasetReference"
                }
            ],
            "outputs": [
                {
                    "referenceName": "SinkDataset",
                    "type": "DatasetReference"
                }
            ],
            "typeProperties": {
                "source": {
                    "type": "BlobSource"
                },
                "sink": {
                    "type": "BlobSink"
                }
            }
        }
    ]
}

# --- Create/Update Pipeline --- #
try:
    pipeline = data_factory_client.pipelines.create_or_update(
        resource_group_name,
        data_factory_name,
        pipeline_name,
        pipeline_definition
    )
    print(f"Pipeline '{pipeline.name}' created/updated successfully.")

    # --- Create Pipeline Run --- #
    create_run_response = data_factory_client.pipelines.create_run(
        resource_group_name,
        data_factory_name,
        pipeline_name
    )
    print(f"Pipeline run initiated with run ID: {create_run_response.run_id}")

except Exception as e:
    print(f"An error occurred: {e}")

This Python code snippet demonstrates how to programmatically create or update a pipeline and trigger a run. In a real-world scenario, you would first set up your linked services (connections to data stores) and datasets (schemas of your data) either through the Azure portal UI or using Azure Resource Manager (ARM) templates ADF ARM template quickstart. The Python SDK then allows for orchestration and management of these resources. For detailed setup and authentication, refer to the Azure Data Factory Python quickstart guide.