What is a valid approach to leverage multiple engines on an FME Server when executing a single workspace?

What is a valid approach to leverage multiple engines on an FME Server when executing a single workspace?

• A. Running the workspace via the Data Streaming service.
• B. Running the single workspace on the same data on multiple engines.
• C. Splitting the workspace into multiple workspaces that can run concurrently.
• D. Running the workspace on multiple FME Server instances.

Answer: (C)

The idea being tested is how to parallelize processing on FME Server for a single workload. A single workspace run is handled by one FME Engine, so to use multiple engines you need to break the work into independent parts that can run at the same time. Splitting the workflow into multiple workspaces that can execute concurrently allows each workspace to be handled by its own engine, effectively distributing the load and increasing throughput on a single server. This approach is especially useful for large datasets or time-sensitive tasks, where partitioning data or steps and triggering parallel runs yields faster results. The Data Streaming service is aimed at moving data in real time rather than parallelizing a single workspace across engines; running the same workspace on the same data across multiple engines doesn’t provide clean, coordinated parallelism within one task. Running the workspace on multiple FME Server instances could scale across servers, but it doesn’t achieve the intended parallel execution of a single workload within one server by using multiple engines in a tightly managed, concurrent fashion.

The idea being tested is how to parallelize processing on FME Server for a single workload. A single workspace run is handled by one FME Engine, so to use multiple engines you need to break the work into independent parts that can run at the same time. Splitting the workflow into multiple workspaces that can execute concurrently allows each workspace to be handled by its own engine, effectively distributing the load and increasing throughput on a single server. This approach is especially useful for large datasets or time-sensitive tasks, where partitioning data or steps and triggering parallel runs yields faster results. The Data Streaming service is aimed at moving data in real time rather than parallelizing a single workspace across engines; running the same workspace on the same data across multiple engines doesn’t provide clean, coordinated parallelism within one task. Running the workspace on multiple FME Server instances could scale across servers, but it doesn’t achieve the intended parallel execution of a single workload within one server by using multiple engines in a tightly managed, concurrent fashion.