TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location, its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US, Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

The most cost-effective approach is to preprocess data locally before consolidating. Launching a Dataproc cluster in each region (US, Europe, Asia) allows for parallel processing and compression of the raw telemetry data close to where it resides. This minimizes data transfer costs, as only the reduced and compressed data needs to be moved. Consolidating this preprocessed data into a single regional bucket (e.g., a US regional bucket if the final Dataproc cluster is also in the US) further optimizes costs by avoiding multi-region storage expenses and ensuring the final processing cluster is co-located with the data. Moving all data into one zone or one region before processing would incur significant egress costs for the initial transfer of 20 million vehicles' telemetry data. Launching a cluster in each region to preprocess and compress, then moving to a multi-region bucket, would be more expensive due to higher multi-region storage costs compared to a single regional bucket for the final consolidated data.