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

The primary use of Cloud Dataproc is to run Apache Spark and Apache Hadoop clusters. This fully managed cloud service allows users to create, manage, and scale clusters for processing large datasets in a distributed computing environment. Dataproc simplifies the deployment of these big data frameworks by automatically configuring and managing the necessary resources, enabling tasks like data processing, analytics, and machine learning.

By utilizing Cloud Dataproc, organizations can benefit from the flexibility and power of Spark and Hadoop, leveraging their capabilities for batch processing, stream processing, and machine learning applications. The service offers seamless integration with other Google Cloud products, making it a versatile solution for data workflows.

In contrast, running NoSQL databases, running data warehouses, and running containerized applications are tasks better suited to services designed specifically for those purposes, such as Cloud Firestore for NoSQL, BigQuery for data warehousing, and Google Kubernetes Engine for containerized applications. Each of these services has distinct functionalities optimized for their respective use cases, while Cloud Dataproc excels in executing big data processing frameworks like Spark and Hadoop.