Your enterprise data team is modernizing its analytics infrastructure on Google Cloud and needs to select appropriate managed compute services for two distinct workloads: Workload 1: Migrate existing Apache Spark and Apache Hadoop MapReduce jobs to the cloud with minimal code refactoring while maintaining support for the open-source Hadoop ecosystem. Workload 2: Build a new pipeline using Apache Beam to execute unified batch and real-time streaming transformations with serverless scaling and zero infrastructure management. Which combination of Google Cloud services should you recommend?

Dataproc for Workload 1 and Dataflow for Workload 2: ### What Dataproc and Dataflow Are **Dataproc** is Google Cloud's managed service for running open-source data processing engines such as **Apache Spark**, **Apache Hadoop**, **Apache Flink**, and **Hive**. **Dataflow** is a fully managed, serverless execution service designed to run **Apache Beam** pipelines for unified stream (real-time) and batch data processing. ### How This Solution Meets the Requirements - **Meets Workload 1 (Spark/Hadoop Migration)**: Dataproc provides native, fully managed Hadoop and Spark clusters, allowing existing jobs, configurations, and ecosystem tools to run seamlessly in the cloud without requiring major code refactoring. - **Meets Workload 2 (Unified Beam Pipelines)**: Dataflow provides fully managed, serverless execution specifically optimized for **Apache Beam** pipelines. It automatically handles resource provisioning, dynamic autoscaling, and pipeline optimization for both continuous streaming and bounded batch data without requiring cluster administration. ### Key Advantages and Capabilities - **Ecosystem Compatibility**: Dataproc allows easy lift-and-shift of legacy big data workloads, retaining tooling such as Spark SQL, PySpark, and MapReduce. - **Serverless Automation**: Dataflow automatically provisions worker nodes, scales dynamically based on pipeline throughput, and manages sharding/fusion optimizations. - **Unified Programming Model**: Dataflow and Apache Beam allow developers to write identical processing logic across batch and streaming ingestion paths. - **Cost Efficiency**: Dataproc offers per-second billing with fast cluster creation and ephemeral cluster models, while Dataflow ensures you only pay for compute resources actively consumed during pipeline execution. ### Why This Is Optimal Choosing Dataproc preserves prior engineering investments in Hadoop/Spark applications, while leveraging Dataflow for new Apache Beam pipelines guarantees true serverless scalability and simplified operations for unified streaming and batch analytics.