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                     What are Integration Runtime Types in Azure Data Factory? 
Introduction 
Integration Runtime ADF acts as the processing engine inside Azure Data 
Factory. This component controls data movement, transformation, and 
connectivity. Beginners often learn this topic first during Azure Data Engineer 
Online Training because every pipeline depends on this engine. 
 
By 2026, Microsoft improved scalability, monitoring, and security features. 
Modern data platforms now rely on Integration Runtime for cloud, hybrid, and 
legacy workloads. 
Table of Contents 
1. Key concepts of Integration Runtime ADF 
2. Types of Integration Runtime in ADF 
3. Key differences between Integration Runtime types 
4. Key examples and benefits for better understanding 
5. Latest updates and trends till 2026 
6. Step-by-step runtime selection guide 
7. FAQs 
1. Key Concepts of Integration Runtime ADF 
Integration Runtime represents compute infrastructure used by Azure Data 
Factory. This engine performs copy activities and data transformations. 
Connectivity across networks also depends on this layer. 
Without Integration Runtime, pipelines cannot execute tasks. Each runtime type 
supports specific data locations and security needs. Understanding this base 
concept prevents design mistakes later. 
2. Types of Integration Runtime in ADF 
Azure Data Factory supports three Integration Runtime types. Each option 
solves a different business problem. Correct selection improves performance 
and reduces operational risk. 
Azure Integration Runtime 
Azure Integration Runtime operates fully inside Microsoft Azure. This option 
supports cloud-to-cloud data transfers. Automatic scaling adjusts compute 
capacity during peak loads. 
Common use cases include Azure SQL, Data Lake, and Synapse Analytics. 
Management effort stays low because Microsoft handles maintenance. 
Many cloud-focused professionals practice this setup during Azure Data 
Engineer Course sessions. 
Self-Hosted Integration Runtime 
Self-hosted Integration Runtime runs on local servers or virtual machines. This 
setup enables secure communication with on-premise systems. Hybrid 
environments benefit most from this configuration. 
Legacy databases often require this approach. Private network access remains 
protected through firewall rules. Visualpath explains this architecture clearly 
using real project demos. 
Azure-SSIS Integration Runtime 
Azure-SSIS Integration Runtime supports SSIS package execution inside 
Azure. Organizations use this option during migration projects. Existing ETL 
workflows move without major redesign. 
Lift-and-shift strategies reduce risk. Enterprise teams continue SSIS usage while 
adopting cloud storage. 
3. Key Differences between Integration Runtime Types 
Azure Integration Runtime focuses on cloud workloads. Self-hosted Integration 
Runtime supports hybrid connectivity. Azure-SSIS Integration Runtime handles 
SSIS execution only. 
Management responsibility differs across options. Scalability remains automatic 
for Azure-based runtimes. Maintenance effort increases for self-hosted 
deployments. 
Understanding these differences helps architects avoid incorrect pipeline 
designs. 
4. Key Examples and Benefits for Better Understanding 
Example 1: Cloud Data Movement 
Sales data moves from Azure Blob Storage to Azure SQL Database. Azure 
Integration Runtime completes this task efficiently. 
Example 2: On-Premise Migration 
Customer records move from local SQL Server to Azure Data Lake. Self-hosted 
Integration Runtime enables secure transfer. 
Example 3: SSIS Workload Migration 
Existing SSIS packages execute inside Azure. Azure-SSIS Integration Runtime 
supports smooth execution. 
Benefits 
Better security. Faster processing. Flexible connectivity. Reduced manual effort. 
Improved reliability. 
5. Latest Updates and Trends Till 2026 
In 2024, Microsoft improved auto-scaling behavior for Azure Integration 
Runtime. Performance tuning became easier for large datasets. 
During 2025, enhanced private endpoint support strengthened network isolation. 
Security compliance improved across regions. 
By 2026, detailed monitoring metrics became available. Cost tracking improved 
for long-running pipelines. These changes made Integration Runtime more 
enterprise-ready. 
6. Step-by-Step Runtime Selection Guide 
Step 1: Identify data source location. 
Cloud or on-premise placement decides the runtime choice. 
Step 2: Review security requirements. 
Private networks require self-hosted configuration. 
Step 3: Check workload type. 
SSIS packages need Azure-SSIS runtime. 
Step 4: Estimate processing scale. 
Elastic workloads suit Azure Integration Runtime. 
Step 5: Validate with test pipelines. 
Performance testing avoids future failures. 
Hands-on practice during Azure Data Engineer Online Training helps 
learners master these decisions. 
7. Security and Governance Considerations 
Integration Runtime defines data access paths. Secure configuration protects 
sensitive information. 
Managed identities reduce credential risks. Network rules restrict unauthorized 
traffic. Regular health checks improve stability. 
Visualpath covers these governance practices during guided lab sessions. 
8. Performance Optimization Tips 
Place runtime resources close to data locations. Reduced latency improves 
throughput. Parallel copy options speed up execution. 
Monitoring resource usage identifies bottlenecks. Scaling nodes improves 
processing speed. Unused runtimes should be removed. 
Performance tuning remains a core topic in Azure Data Engineer Course 
learning paths. 
9. Career and Learning Perspective 
Integration Runtime knowledge remains essential for Azure data roles. 
Interview discussions often include runtime selection scenarios. 
Practical exposure builds confidence. Real pipelines highlight architectural 
challenges. 
Visualpath supports learners through structured mentoring and project-based 
learning. 
FAQs 
1Q. What is an integration runtime and its types? 
A. Integration runtime provides compute for ADF pipelines. Azure, Self-hosted, 
and Azure-SSIS are the main types. Visualpath explains each clearly. 
2Q. What are the three types of triggers in ADF? 
A. Schedule trigger, Tumbling Window trigger, and Event trigger automate 
pipeline execution. 
3Q. What is the difference between Azure and self-hosted integration 
runtime in ADF? 
A. Azure runtime supports cloud data movement. Self-hosted runtime connects 
on-premise systems securely. 
4Q. What is purview integration runtime? 
A. Purview integration runtime supports data scanning and governance across 
hybrid environments. 
Conclusion 
Integration Runtime forms the backbone of Azure Data Factory pipelines. 
Correct runtime selection ensures security, scalability, and cost efficiency. 
Updates till 2026 strengthened hybrid connectivity and monitoring features. 
Early mastery of this concept builds a strong data engineering foundation. 
Visualpath stands out as the best online software training institute in 
Hyderabad. 
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