Detailed Explanation

In Azure Data Factory, when a Copy activity involves data movement between different integration runtime types, the execution location follows specific precedence rules. According to Microsoft documentation:

• When source and sink use different integration runtimes, the copy activity executes on the self-hosted integration runtime if either the source or sink is connected to one.

Analysis of Options:

Option A: Scale out the self-hosted integration runtime ✅

• Optimal Choice: Since Copy1's source is on-premises SQL Server using self-hosted IR, the copy execution occurs on the self-hosted IR. Scaling out (adding more nodes) increases parallel processing capacity and throughput.
• Minimizes Administrative Effort: Scaling out self-hosted IR involves adding nodes to the existing cluster, which is less administratively intensive than managing Azure infrastructure changes.

Option B: Scale up the data flow runtime of the Azure integration runtime AND scale out the self-hosted integration runtime ❌

• Inefficient: Scaling up Azure IR's data flow runtime is unnecessary since copy activities don't use data flow runtime resources. This adds administrative overhead without performance benefits.
• Violates Minimization Principle: Combines unnecessary actions, increasing administrative effort.

Option C: Scale up the data flow runtime of the Azure integration runtime ❌

• Incorrect Approach: Copy activities execute on integration runtimes, not data flow runtimes. Scaling data flow runtime has no impact on copy performance.
• No Benefit: Since the copy executes on self-hosted IR, Azure IR scaling provides zero performance improvement.

Key Technical Points:

1. Integration Runtime Selection: Copy activities use the self-hosted IR when either source or sink requires it.
2. Self-Hosted IR Scaling: Scaling out (horizontal scaling) increases parallel data movement capacity.
3. Administrative Efficiency: Self-hosted IR scaling requires only node addition to existing infrastructure, aligning with minimal effort requirement.

Therefore, scaling out the self-hosted integration runtime directly addresses the compute resource limitation while maintaining minimal administrative overhead.