Google Cloud Professional Data Engineer Exam 2025 – 400 Free Practice Questions to Pass the Exam

Google Cloud Dataflow is predominantly used for batch processing as it is designed to handle both stream and batch data processing seamlessly. It operates on the Apache Beam model, which allows developers to define complex data processing workflows. This is particularly beneficial for executing transformations on large datasets, enabling parallel processing and dynamic scaling.

Dataflow efficiently handles batch jobs by utilizing a worker pool that can scale up or down based on the data volume and workload, optimizing resource use and minimizing costs. Batch processing scenarios often require processing large volumes of historical data, which Dataflow can accomplish through its SDK that supports various programming languages.

In contrast, while Google BigQuery is a powerful data warehouse optimized for querying large datasets, it focuses more on analytics and querying rather than processing workflows in a traditional sense. Google Cloud Pub/Sub is primarily a messaging service used for achieving real-time communication between services, and Google Cloud Functions is geared towards executing event-driven serverless functions and doesn’t specifically target batch processing workloads. Thus, Dataflow’s capabilities make it the go-to option for batch processing tasks within Google Cloud.