What strategy is recommended to increase throughput when you need to process large datasets with FME?

• A. Running the workspace on a single engine with more RAM
• B. Running the workspace via the Data Streaming service
• C. Splitting the workspace into multiple workspaces that can run concurrently
• D. Merging data before processing

Answer: (C)

Maximizing throughput for large datasets in FME comes from running independent parts of the workload at the same time. Splitting the workflow into multiple workspaces that can run concurrently lets you partition the data and process each chunk in parallel, making full use of all CPU cores and I/O bandwidth. After processing, you can combine the results as needed. This parallel approach scales with available hardware and can be orchestrated by FME Server or multiple engine instances, delivering much higher throughput than a single, monolithic run.

Relying on a single engine with more RAM addresses memory limits but doesn’t increase parallel processing, so throughput gains are limited. The Data Streaming service can be great for continuous data flow, but it isn’t the general solution for bulk throughput. Merging data before processing adds extra I/O and a consolidation step, which can become a bottleneck and reduce overall throughput.