Table of Contents
What is Azure Data Factory?
Azure Data Factory is a cloud-based data integration service that allows you to visually integrate data sources with more than 90 built-in, maintenance-free connectors at no added cost, and easily construct extract, transform, and load (ETL) and extract, load, and transform (ELT) processes code-free in an intuitive environment or write your own code. As a managed service provided by Microsoft Azure, it facilitates data ingestion, preparation, and transformation across a wide range of data sources, including on-premises databases, cloud services, and SaaS applications. ADF is designed to help data engineers and analysts automate data workflows efficiently, making it a vital tool for modern data integration and analytics initiatives.
Azure Data Factory empowers organizations to build sophisticated data pipelines that can handle complex data movement and transformation scenarios at scale. Whether you’re migrating legacy systems to the cloud, consolidating data from multiple sources, or preparing data for advanced analytics and machine learning, ADF provides the flexibility and power needed to accomplish these tasks without managing underlying infrastructure.
Core Components of Azure Data Factory
Understanding the core components of Azure Data Factory is essential for building effective data integration solutions. These building blocks work together to create robust, scalable data pipelines.
Pipeline
A pipeline is a logical grouping of activities that defines a series of tasks to perform data movement and transformation. Pipelines represent the core unit of work in Azure Data Factory, orchestrating the flow of data from source to destination while applying necessary transformations along the way. You can schedule pipelines to run at specific times, trigger them based on events, or execute them on demand.
Activity
Activities define the actions to perform on your data, and an activity is a unit of orchestration in Azure Data Factory. Each activity represents a task within a pipeline, such as copying data from one location to another, executing a stored procedure, running a data transformation, or invoking an external service. Activities can take zero or more datasets as inputs and produce one or more datasets as output, allowing you to chain multiple activities together to create complex workflows.
Dataset
Datasets represent data structures within your data stores, such as tables, files, folders, or documents. They serve as named references to the data you want to use in your activities as inputs or outputs. Datasets don’t contain the actual data but rather point to or reference the data you want to use in your pipelines. This abstraction allows you to reuse dataset definitions across multiple pipelines and activities.
Linked Service
Linked services contain connection information to access data sources, and entities include datasets, linked services, pipelines, integration runtime, and triggers. Linked services are similar to connection strings, defining the connection information needed for Azure Data Factory to connect to external resources. They encapsulate authentication credentials, connection endpoints, and other configuration details required to establish connectivity with your data sources and destinations.
Integration Runtime
Data Factory offers three types of Integration Runtime (IR), and you should choose the type that best serves your data integration capabilities and network environment requirements. The three types of IR are Azure, Self-hosted, and Azure-SSIS. The Integration Runtime provides the compute infrastructure used to execute activities in your pipelines. It serves as the bridge between your activities and your data sources, handling data movement, transformation, and activity dispatch.
Trigger
Triggers are scheduling or event-based mechanisms that determine when a pipeline execution should be initiated. Azure Data Factory supports several types of triggers, including schedule triggers that run pipelines at specified intervals, tumbling window triggers for processing data in time-based windows, and event-based triggers that respond to specific events such as file arrivals in blob storage. This flexibility allows you to automate pipeline execution based on your specific business requirements.
Understanding Integration Runtime Types
The Integration Runtime is a critical component that determines how and where your data integration activities are executed. Choosing the right Integration Runtime type is essential for optimizing performance, security, and cost.
Azure Integration Runtime
The Azure Integration Runtime (Azure IR) is the default option in Azure Data Factory for running activities in a fully managed, serverless environment. It eliminates the need for any hardware or infrastructure setup, making it easy to start without maintenance overhead. Azure IR automatically scales based on workload demands, ensuring optimal performance for both small and large data operations. This runtime is ideal for cloud-to-cloud data movement scenarios, such as copying data between Azure Blob Storage and Azure SQL Database, or transferring data from Amazon S3 to Azure Data Lake Storage.
The Azure integration runtime has the highest concurrency support among all integration runtime types. Data integration unit (DIU) is the unit of capability to run on Azure Data Factory. You can configure the number of DIUs for copy activities to optimize performance based on your data volume and complexity requirements.
Self-Hosted Integration Runtime
The Self-Hosted Integration Runtime (Self-Hosted IR) in Azure Data Factory is designed to access on-premises data sources or data within private networks that are not reachable via public endpoints. It is installed as an application on your own machine or virtual machine and can be configured as part of a high-availability cluster to ensure reliability. This runtime supports both data movement and Data Flow execution within private networks, making it ideal for secure, internal data transfers.
Self-Hosted IR is particularly useful for hybrid scenarios where you need to integrate on-premises data sources with cloud-based systems. For example, you might use it to copy data from an on-premises SQL Server to Azure Data Lake, or to transfer data between two databases behind corporate firewalls. Unlike Azure IR, the Self-Hosted IR requires your team to handle installation, monitoring, updates, and maintenance of the runtime software and the underlying infrastructure.
Azure-SSIS Integration Runtime
The Azure-SSIS Integration Runtime (Azure-SSIS IR) is a specialized runtime in Azure Data Factory designed to run SQL Server Integration Services (SSIS) packages. It enables a seamless lift-and-shift approach, allowing you to migrate existing on-premises SSIS ETL packages to Azure without the need for rewriting or major modifications. This runtime can integrate with Azure SQL Managed Instance or Azure SQL Database and supports connectivity to both cloud-based and on-premises data sources.
Azure Data Factory helps organizations looking to modernize SQL Server Integration Services (SSIS). Gain up to 88% cost savings with Azure Hybrid Benefit. Enjoy the only fully compatible data integration service that makes it easy to move all your SSIS packages to the cloud. This makes Azure-SSIS IR an excellent choice for organizations with existing SSIS investments who want to leverage cloud scalability and management while preserving their existing ETL logic.
Mapping Data Flows: Code-Free Transformations
Mapping data flows are visually designed data transformations in Azure Data Factory. Data flows allow data engineers to develop data transformation logic without writing code. The resulting data flows are executed as activities within Azure Data Factory pipelines that use scaled-out Apache Spark clusters. This powerful feature democratizes data transformation, making it accessible to users with varying levels of technical expertise.
Visual Design Experience
Mapping data flows provide an entirely visual experience with no coding required. Your data flows run on ADF-managed execution clusters for scaled-out data processing. The visual interface includes a canvas where you can design transformation logic by connecting various transformation components, a configuration panel for setting transformation properties, and real-time data preview capabilities that allow you to validate your transformations as you build them.
Transformation Categories
In Azure Data Factory (ADF), Mapping Data Flows organize transformations into several functional categories. These categories help you navigate the interface and understand how each tool impacts your data stream. The transformation categories include:
- Multiple inputs/outputs: Join, Conditional Split, Exists, Union, Lookup, and New Branch transformations that allow you to combine or split data streams
- Schema modifiers: Derived Column, Select, Aggregate, Pivot, Unpivot, Window, and Rank transformations that modify the structure or content of your data
- Row modifiers: Filter, Sort, Alter Row, and Assert transformations that focus on filtering or tagging individual rows
- Formatters: Flatten, Parse, and Stringify transformations for handling complex, hierarchical, or semi-structured data types like JSON, XML, and arrays
Performance and Scalability
Azure Data Factory handles all the code translation, path optimization, and execution of your data flow jobs. Behind the scenes, mapping data flows leverage Apache Spark for distributed processing, which means they can scale to handle massive datasets efficiently. Azure Data Factory is updating Mapping Data Flows to use Spark 3.3, providing improved performance and access to the latest Spark capabilities.
Creating a Data Pipeline in Azure Data Factory
Building a data pipeline in Azure Data Factory involves several key steps that work together to create an end-to-end data integration solution. Here’s a comprehensive walkthrough of the pipeline creation process.
Step 1: Define Linked Services
The first step is to create linked services that establish connections to your data sources and destinations. This involves specifying connection strings, authentication methods, and any other configuration details required to access your data stores. For example, you might create a linked service for Azure Blob Storage using account key authentication, or a linked service for an on-premises SQL Server using SQL authentication through a Self-Hosted Integration Runtime.
Step 2: Create Datasets
Once your linked services are configured, you create datasets that represent the specific data structures you’ll be working with. Datasets reference the linked services and specify additional details such as file paths, table names, schemas, and data formats. For instance, you might create a dataset pointing to a specific CSV file in Azure Blob Storage, or a dataset representing a table in Azure SQL Database.
Step 3: Design the Pipeline
With your linked services and datasets in place, you can design your pipeline by adding and configuring activities. The pipeline canvas provides a drag-and-drop interface where you can add activities such as Copy Data, Data Flow, Stored Procedure, or Web activities. You can chain activities together, add conditional logic, implement loops, and create complex orchestration patterns to meet your specific requirements.
Step 4: Configure Triggers
After designing your pipeline, you configure triggers to automate its execution. You might create a schedule trigger to run the pipeline daily at 2 AM, a tumbling window trigger to process data in hourly batches, or an event-based trigger that executes the pipeline whenever a new file arrives in a specific blob container. Triggers can also be parameterized to pass dynamic values to your pipeline at runtime.
Step 5: Test and Debug
Before publishing your pipeline to production, it’s essential to test and debug it thoroughly. Azure Data Factory provides a debug mode that allows you to execute pipelines without publishing them, enabling you to validate your logic and troubleshoot issues. You can set breakpoints, inspect intermediate data, and review detailed execution logs to ensure your pipeline works as expected.
Step 6: Monitor and Optimize
In Azure Data Factory, monitor all your activity runs visually and improve operational productivity by setting up alerts proactively to monitor your pipelines. The monitoring interface provides detailed information about pipeline runs, activity execution times, data volumes processed, and any errors or warnings that occurred. You can use this information to identify bottlenecks, optimize performance, and ensure your pipelines are running reliably.
Benefits of Using Azure Data Factory
Azure Data Factory offers numerous advantages that make it a compelling choice for organizations seeking to modernize their data integration infrastructure.
Scalability and Performance
Azure Data Factory is built to handle data integration at any scale. Whether you’re processing gigabytes or petabytes of data, ADF can scale dynamically to meet your needs. The service automatically provisions and manages the compute resources required for your workloads, eliminating the need for capacity planning and infrastructure management. This serverless architecture ensures you have the resources you need when you need them, without paying for idle capacity.
Extensive Integration Capabilities
Azure Data Factory supports visually integrating data sources with more than 90 built-in, maintenance-free connectors at no added cost, and you can choose from more than 90 built-in connectors to acquire data from big data sources such as Amazon Redshift, Google BigQuery, and Hadoop Distributed File System (HDFS); enterprise data warehouses such as Oracle Exadata and Teradata; software as a service (SaaS) apps such as Salesforce, Marketo, and ServiceNow. This extensive connector library enables you to integrate data from virtually any source, whether it’s in the cloud, on-premises, or in a SaaS application.
Automation and Orchestration
Azure Data Factory excels at automating complex data workflows. You can schedule pipelines to run at specific times, trigger them based on events, or execute them on demand. The service supports sophisticated orchestration patterns including conditional execution, looping, branching, and error handling. Pipeline activity limit lifted to 80 activities, giving you even more flexibility to build complex workflows within a single pipeline.
Comprehensive Monitoring and Alerting
Azure Data Factory provides detailed logs and monitoring capabilities that give you complete visibility into your data integration processes. You can track pipeline runs, monitor activity execution, view data lineage, and set up alerts to notify you of failures or performance issues. Integration with Azure Monitor and Log Analytics enables advanced diagnostics and long-term retention of monitoring data for compliance and auditing purposes.
Cost-Effective Pricing Model
Azure Data Factory pricing is consumption-based with charges for activity runs, data movement (DIUs), transformations (vCore-hours), and operations like monitoring and management. Azure Data Factory offers a single, pay-as-you-go service, which means you only pay for what you use. This consumption-based model can be more cost-effective than maintaining on-premises infrastructure or paying for fixed-capacity cloud services, especially for workloads with variable or unpredictable data volumes.
Hybrid and Multi-Cloud Support
Azure Data Factory is designed to support hybrid and multi-cloud scenarios. With the Self-Hosted Integration Runtime, you can securely connect to on-premises data sources and integrate them with cloud-based systems. The service also supports cross-cloud data movement, allowing you to move data between Azure and other cloud platforms like AWS and Google Cloud Platform, enabling true multi-cloud data integration strategies.
Enterprise-Grade Security and Compliance
Security is built into every layer of Azure Data Factory. The service supports various authentication methods including managed identities, service principals, and Azure Key Vault integration for secure credential management. Data in transit is encrypted using industry-standard protocols, and you can leverage Azure Private Link for private connectivity to your data sources. Azure Data Factory also complies with major industry standards and regulations, making it suitable for highly regulated industries.
Azure Data Factory Pricing Explained
Understanding Azure Data Factory pricing is crucial for budgeting and cost optimization. The pricing model is consumption-based, with charges accumulating across multiple dimensions.
Pipeline Orchestration and Execution
You pay for data pipeline orchestration by activity run and activity execution by integration runtime hours. The integration runtime, which is serverless in Azure and self-hosted in hybrid scenarios, provides the compute resources used to execute the activities in a pipeline. Activity runs are charged based on the number of executions, while integration runtime hours are charged based on the compute resources consumed during execution.
Data Movement Costs
Microsoft calculates data movement costs using Data Integration Units (DIUs), which vary based on data volume, complexity, and whether data crosses regions. Azure costs $0.25 per data integration unit, equivalent to a DPU. The number of DIUs used depends on factors such as the size of the data being moved, the complexity of the copy operation, and the performance characteristics of the source and destination data stores.
Data Flow Execution
Mapping Data Flows are charged based on vCore-hours, which represent the compute resources consumed during data transformation operations. The cost depends on the compute type (general purpose or memory optimized), the number of vCores allocated, and the execution duration. You can optimize costs by right-sizing your data flow compute configurations and enabling time-to-live (TTL) settings to keep clusters warm between executions.
Data Factory Operations
Read/Write operations are priced at $0.50 per 50,000 modified or referenced entities, and monitoring operations are priced at $0.25 per 50,000 retrieved run records, covering retrieval of pipeline, activity, trigger, and debug-run monitoring information. These operational costs are typically minimal compared to pipeline execution and data movement costs, but they should still be considered when estimating total costs.
Cost Optimization Strategies
Use the ADF pricing calculator to get an estimate of the cost of running your ETL workload in Azure Data Factory. To use the calculator, you have to input details such as number of activity runs, number of data integration unit hours, type of compute used for Data Flow, core count, instance count, execution duration, and etc. Additional cost optimization strategies include:
- Consolidating multiple small pipelines into fewer, more efficient pipelines
- Using parameterization to create reusable pipeline templates
- Enabling TTL on Integration Runtimes to reduce cluster startup times
- Right-sizing DIU and vCore allocations based on actual workload requirements
- Scheduling non-time-sensitive workloads during off-peak hours
- Monitoring and eliminating idle or unused pipelines and resources
Azure Data Factory vs. AWS Glue: A Comparison
When evaluating cloud-based ETL services, Azure Data Factory and AWS Glue are two of the most popular options. Understanding their differences can help you choose the right tool for your organization.
Architecture and Design Philosophy
Transferring and transforming data for analysis often requires ETL tools, and AWS Glue and Azure Data Factory offer two distinct methods. Glue leans on a code-driven approach, while Data Factory emphasizes a visual, drag-and-drop design. Your choice boils down to whether you prefer scripting or a more visual pipeline-building experience. AWS Glue is primarily designed for ETL operations with a focus on code-based transformations using PySpark or Scala, while Azure Data Factory provides a more comprehensive data integration platform with both visual and code-based options.
Pricing Models
AWS Glue’s pay-per-use model is straightforward, charging based on Data Processing Units (DPUs) per job execution. Azure Data Factory separates pricing into pipeline orchestration, data movement, and transformation costs, which can be more flexible for simple workflows but expensive for complex ETL processes. Glue’s pricing model is more standardized and, as a result, likely more predictable. Glue charges mainly by data processing unit (DPU) hours, while Azure Data Factory has multiple cost components that can make budgeting more complex.
Integration and Ecosystem
Azure Data Factory is best for organizations already using Microsoft tools like SQL Server, Power BI, and Active Directory. Azure Data Factory and Synapse Analytics offer strong integration and a user-friendly, visual interface. AWS Glue, on the other hand, integrates seamlessly with the AWS ecosystem including S3, Redshift, Athena, and other AWS services. Your existing technology stack and cloud platform preference will often be the deciding factor.
SSIS Package Support
ADF provides native support for SSIS packages so its easier to migrate SSIS packages unlike AWS Glue that does not provide native support. Importing SSIS packages to AWS Glue takes more time and effort than Azure Data Factory. Glue requires conversion of packages, but Azure Data Factory allows users to install and use SSIS packages directly without converting or migrating them. This makes Azure Data Factory the clear choice for organizations with existing SSIS investments.
Scalability and Workload Management
The two platforms also differ in how they manage workloads. AWS Glue is fully serverless, scaling resources automatically based on demand. Azure Data Factory relies on Integration Runtimes, which provide manual control over execution environments – making it a strong choice for hybrid setups that bridge cloud and on-premises systems. Both platforms can handle large-scale data processing, but they approach scalability differently.
Best Practices for Using Azure Data Factory
Implementing best practices ensures your Azure Data Factory solutions are reliable, maintainable, and cost-effective. Here are key recommendations for building production-grade data integration pipelines.
Design Modular and Reusable Pipelines
Create modular pipelines that focus on specific tasks rather than building monolithic pipelines that try to do everything. Use pipeline parameters to make your pipelines flexible and reusable across different scenarios. For example, instead of creating separate pipelines for each table you need to copy, build a single parameterized pipeline that accepts table names and connection details as parameters. This approach reduces maintenance overhead and ensures consistency across your data integration processes.
Implement Robust Error Handling
Design your pipelines with comprehensive error handling to improve reliability and reduce manual intervention. Use try-catch patterns with the Execute Pipeline activity, configure retry policies for transient failures, and implement proper logging to capture detailed error information. Set up alerts to notify the appropriate teams when critical failures occur, and design your pipelines to handle partial failures gracefully without corrupting data or leaving systems in inconsistent states.
Leverage Parameterization and Dynamic Content
Instead of creating ten table-specific pipelines with redundant logic, build one parameterized pipeline that handles all ten tables by passing table names, query conditions, and connection strings as runtime variables. This reduces maintenance overhead and lowers execution costs. Dynamic content in pipelines adapts to changing conditions without requiring manual intervention or additional pipeline variants. Use expressions, variables, and parameters extensively to create flexible, data-driven pipelines.
Secure Your Data and Credentials
Never hardcode credentials or sensitive information in your pipelines. Instead, store credentials in Azure Key Vault and reference them through linked services. Use managed identities whenever possible to eliminate the need for credential management altogether. Implement proper access controls using Azure role-based access control (RBAC) to ensure users and service principals have only the permissions they need. Enable data encryption in transit and at rest, and use Private Link for secure connectivity to data sources.
Optimize Integration Runtime Configuration
For operationalized pipelines, it’s highly recommended that you create your own Azure Integration Runtimes that define specific regions, compute type, core counts, and TTL for your data flow activity execution. A minimum compute type of General Purpose with an 8+8 (16 total v-cores) configuration and a 10-minute Time to live (TTL) is the minimum recommendation for most production workloads. By setting a small TTL, the Azure IR can maintain a warm cluster that won’t incur the several minutes of start time for a cold cluster. Proper IR configuration can significantly improve performance and reduce costs.
Monitor and Optimize Performance
Regularly review pipeline execution metrics to identify performance bottlenecks and optimization opportunities. Use the consumption monitoring features to understand which activities are consuming the most resources and costing the most money. Optimize data movement by using appropriate DIU settings, partition your data for parallel processing, and consider using incremental loading patterns instead of full refreshes when possible. Monitor Integration Runtime utilization and adjust configurations based on actual usage patterns.
Implement CI/CD and Version Control
Integrate Azure Data Factory with Git (Azure DevOps or GitHub) to enable version control, collaboration, and CI/CD practices. Azure Data Factory now supports Azure DevOps Server 2022 for Git integration, including on-premises ADO server. Use separate Data Factory instances for development, testing, and production environments. Implement automated deployment pipelines that validate and test your changes before promoting them to production. This approach reduces the risk of errors and makes it easier to roll back changes if issues arise.
Document Your Pipelines and Processes
Maintain comprehensive documentation for your data integration processes. Use meaningful names for pipelines, activities, datasets, and linked services that clearly describe their purpose. Add descriptions and annotations to complex logic to help other team members understand your implementation. Document dependencies, data lineage, and business logic to facilitate troubleshooting and knowledge transfer. Good documentation is essential for maintaining data integration solutions over time, especially as team members change.
Advanced Features and Capabilities
Beyond the core functionality, Azure Data Factory offers several advanced features that enable sophisticated data integration scenarios.
Change Data Capture (CDC)
Azure Data Factory supports change data capture capabilities that allow you to efficiently track and process only the data that has changed since the last pipeline run. This is particularly useful for incremental data loading scenarios where you want to minimize data movement and processing time. CDC can be implemented using various techniques including watermark columns, change tracking features in source databases, or native CDC connectors.
Data Flow Debug Mode
The data flow debug mode provides an interactive development experience where you can build and test transformations against live data without executing the entire pipeline. This feature significantly accelerates development by allowing you to preview data at each transformation step, validate your logic in real-time, and troubleshoot issues quickly. Debug sessions maintain a warm Spark cluster, enabling fast iteration during development.
Managed Virtual Network
Azure Data Factory supports managed virtual networks that provide network isolation and enhanced security for your data integration workloads. General Availability of Time to Live (TTL) for Managed Virtual Network enables you to control how long the managed virtual network resources remain active, optimizing both security and cost. Managed virtual networks allow you to create private endpoints to your data sources, ensuring data never traverses the public internet.
Schema Drift Handling
Mapping Data Flows include built-in capabilities to handle schema drift, which occurs when source data schemas change over time. You can configure data flows to automatically detect and adapt to schema changes, making your pipelines more resilient to evolving data structures. This is particularly valuable in scenarios where you don’t have complete control over source data schemas or when integrating with external systems that may change without notice.
Tumbling Window Triggers
Tumbling window triggers enable you to process data in fixed-size, non-overlapping time windows. This is ideal for scenarios where you need to process data in regular intervals (hourly, daily, etc.) and maintain dependencies between consecutive windows. Tumbling window triggers support backfilling, allowing you to reprocess historical time windows if needed, and they automatically pass window start and end times as parameters to your pipelines.
Integration with Azure Synapse Analytics
Ingest data from on-premises, hybrid, and multicloud sources, and transform it with powerful data flows in Azure Synapse Analytics, powered by Data Factory. Integrate and transform data in the familiar Data Factory experience within Azure Synapse Pipelines. Then transform and analyze data code-free with data flows within the Azure Synapse studio. This tight integration enables unified analytics experiences that combine data integration, data warehousing, and big data analytics in a single platform.
Real-World Use Cases
Azure Data Factory excels in various real-world scenarios across different industries and use cases.
Data Warehouse Modernization
Organizations use Azure Data Factory to modernize their data warehousing infrastructure by migrating from on-premises systems to cloud-based solutions. ADF can orchestrate the migration of historical data, implement incremental loading patterns for ongoing data synchronization, and transform data to match new schema designs. The service’s ability to handle both batch and streaming data makes it suitable for building modern, real-time data warehouses.
Data Lake Ingestion
Azure Data Factory is commonly used to ingest data from multiple sources into data lakes for analytics and machine learning. Organizations can build pipelines that collect data from operational databases, SaaS applications, IoT devices, and external APIs, then land that data in Azure Data Lake Storage in various formats (Parquet, Delta Lake, etc.). The service supports schema-on-read patterns and can handle both structured and unstructured data.
Hybrid Data Integration
Many enterprises operate in hybrid environments with data spread across on-premises systems and multiple clouds. Azure Data Factory’s Self-Hosted Integration Runtime enables secure connectivity to on-premises data sources, allowing organizations to build hybrid data integration solutions that bridge cloud and on-premises environments. This is particularly valuable during cloud migration journeys where systems need to coexist for extended periods.
Business Intelligence and Reporting
Azure Data Factory plays a crucial role in preparing data for business intelligence and reporting solutions. Organizations use ADF to extract data from operational systems, apply business logic and transformations, and load the processed data into analytical databases or data warehouses. The transformed data can then be consumed by BI tools like Power BI, Tableau, or custom reporting applications to deliver insights to business users.
Machine Learning Data Preparation
Data scientists and ML engineers use Azure Data Factory to automate data preparation pipelines for machine learning projects. ADF can orchestrate data collection from multiple sources, perform feature engineering transformations, handle data quality checks, and deliver prepared datasets to machine learning platforms like Azure Machine Learning. This automation ensures consistent, repeatable data preparation processes that are essential for reliable ML model training and deployment.
Migration from Legacy ETL Tools
Organizations with existing ETL investments often need to migrate to cloud-based solutions. Azure Data Factory provides several pathways for this migration.
SSIS Migration
For organizations using SQL Server Integration Services, Azure Data Factory offers a straightforward migration path through the Azure-SSIS Integration Runtime. You can lift and shift existing SSIS packages to Azure with minimal modifications, then gradually modernize them to use native ADF capabilities over time. This phased approach allows organizations to realize cloud benefits quickly while minimizing disruption to existing processes.
Fabric Migration Assistant
The new Fabric migration assistant for Azure Data Factory and Synapse Analytics helps move your existing pipelines and artifacts like Spark pools and notebooks into Fabric with minimal disruption. It’s designed to support incremental modernization, allowing teams to evaluate, convert, and optimize pipelines as they transition to Fabric. This tool simplifies the migration process for organizations looking to adopt Microsoft Fabric’s unified analytics platform.
Assessment and Planning
Before migrating from legacy ETL tools, conduct a thorough assessment of your existing data integration landscape. Inventory all ETL jobs, document dependencies, identify data sources and destinations, and understand current performance characteristics. This assessment w