Migrate a fluctuating, peak-sized on-prem Spark/Hive/HDFS cluster to the cloud within 2 months, reduce ops, and enable later serverless modernization. Which migration approach balances speed and cost savings?

Migrating to Dataproc with Cloud Storage is the most balanced approach. Dataproc offers a managed Spark and Hadoop service, reducing operational overhead immediately. Using Cloud Storage instead of HDFS decouples storage from compute, providing better scalability, durability, and cost-efficiency, and is a crucial step towards serverless modernization. This approach allows for a rapid lift-and-shift of existing Spark/Hive workloads within the 2-month timeframe while setting the stage for future serverless transformations. Migrating to Dataproc preserving HDFS would retain a managed HDFS, but it still ties storage to compute, limiting scalability and increasing operational complexity compared to Cloud Storage. Modernizing Hive to BigQuery while keeping Spark on Dataproc with HDFS is a partial modernization that doesn't fully address the HDFS dependency or the desire for future serverless. Immediately modernizing Spark to Dataflow and Hive to BigQuery is a significant re-architecture that is unlikely to be completed within a 2-month timeframe, especially for a fluctuating, peak-sized cluster.