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?
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A. B. C. D.B.
Google Cloud Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform.
Cloud Dataproc provisions big or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud Platform services, such as Google Cloud Storage and Stackdriver Logging, thus helping you reduce TCO.
https://cloud.google.com/dataproc/docs/resources/faqThe best solution for this scenario would be Google Cloud Dataproc (option B).
Google Cloud Dataproc is a fully-managed cloud service for running Apache Spark and Hadoop clusters. It provides a fast, easy, and cost-effective way to run big data workloads in the cloud. With Dataproc, you can easily create, configure, and manage Spark and Hadoop clusters without having to worry about the underlying infrastructure.
Dataproc provides a number of benefits that make it ideal for scaling big data workloads in the cloud:
Easy to use: Dataproc makes it easy to create and manage Spark and Hadoop clusters. You can easily set up clusters of any size and configuration, and Dataproc will handle all the underlying infrastructure.
Scalability: Dataproc is designed to scale up and down as needed, so you can easily handle fluctuations in workload. This means that you can easily scale your big data workloads to meet your needs without having to worry about capacity planning.
Cost-effective: Dataproc is a cost-effective solution for running big data workloads in the cloud. You only pay for the resources you use, and Dataproc provides automatic cluster scaling and shutdown to minimize costs.
Integration: Dataproc integrates with a number of other Google Cloud Platform services, including Google Cloud Storage, BigQuery, and Cloud SQL. This makes it easy to move data between services and run complex data pipelines.
Google Cloud Dataflow (option A) is a managed service for processing data in batch and stream mode. While it can also process Apache Spark jobs, it is not as well-suited for running large-scale Spark and Hadoop clusters.
Google Compute Engine (option C) is an Infrastructure-as-a-Service (IaaS) solution that provides virtual machines (VMs) for running applications in the cloud. While you can use Compute Engine to run Spark and Hadoop clusters, it requires more operations work and management compared to Dataproc.
Google Kubernetes Engine (option D) is a managed service for running containerized applications on Kubernetes clusters. While it can also run Spark and Hadoop applications, it is not as well-suited for managing and scaling large-scale Spark and Hadoop clusters.