Your company is forecasting a sharp increase in the number and size of Apache Spark and Hadoop jobs being run on your local datacenter. You want to utilize the cloud to help you scale this upcoming demand with the least amount of operations work and code change. Which product should you use?

Google Cloud Dataproc is the best choice because it is a fully managed, scalable service for running Apache Spark and Hadoop clusters. It allows you to easily provision and manage clusters, scaling resources up or down as needed, which directly addresses the forecasted increase in job demand. It also minimizes operational overhead and requires no code changes for existing Spark/Hadoop jobs, as it's a managed service compatible with these open-source frameworks. Google Cloud Dataflow is a fully managed service for stream and batch data processing, but it uses its own SDK (Apache Beam) and would require code changes for existing Spark/Hadoop jobs. Google Compute Engine offers virtual machines, but managing Spark/Hadoop clusters on GCE would involve significant operational work. Google Kubernetes Engine is a container orchestration platform, which would also require substantial setup and management for Spark/Hadoop, increasing operational burden.