Exam Professional Cloud Architect topic 1 question 18 discussion

"B. Google Cloud Dataproc" is the answer

Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data. https://cloud.google.com/dataproc/docs/concepts/overview#:~:text=Dataproc%20is%20a%20managed%20Spark,%2C%20streaming%2C%20and%20machine%20learning.&text=With%20less%20time%20and%20money,your%20jobs%20and%20your%20data.

1. Managed Hadoop and Spark: Dataproc is specifically designed for running and managing Apache Spark and Hadoop clusters, which directly addresses your company's needs. 2. Scalability: Dataproc allows you to easily scale your clusters to handle the increasing number and size of jobs. You can add or remove nodes as needed to accommodate the workload. 3. Minimal Operations Work: Dataproc automates cluster creation, configuration, and management, minimizing the operational overhead. This is crucial since you want to reduce operations work. 4. Code Compatibility: Dataproc is compatible with existing Spark and Hadoop code, so you can migrate your jobs with minimal or no code changes.

B is correct because Dataproc is uded for Apache Hadoop and Spark

Dataflow for data stream and batch. Dataproc for data process with Apache Spark and Hadoop. Compute Engine for VM. Kubernetes Engine for Kubernetes Cluster with Compute Engine under the hood.

B, Dataproc is Hadoop/Spark managed service in GCP

Dataproc is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks. Use Dataproc for data lake modernization, ETL, and secure data science, at scale, integrated with Google Cloud, at a fraction of the cost. https://cloud.google.com/dataproc

To scale the number and size of Apache Spark and Hadoop jobs being run on a local datacenter with the least amount of operations work and code change, you should consider using Google Cloud Dataproc, option B. Google Cloud Dataproc is a fully-managed service that makes it easy to run Apache Spark and Hadoop workloads in the cloud. It is designed to simplify the process of setting up and managing clusters for data processing, and allows you to scale quickly and easily as demand increases. With Cloud Dataproc, you can create and delete clusters in just a few minutes, and you can use the familiar Apache Spark and Hadoop APIs and tools to process data. This means that you can utilize the cloud to scale your workloads with minimal changes to your code and operations work. Option A: Google Cloud Dataflow, option C: Google Compute Engine, and option D: Google Kubernetes Engine, would not be suitable for this use case, as they do not provide the same level of support for running Apache Spark and Hadoop workloads as Cloud Dataproc.

B. Dataproc is managed Apache Spark and Hadoop in GCP

Dataproc for Hadoop and spark ecosystem

B. Google Cloud Dataproc

B data proc for hadoop and spark

Keyword - Apache Spark and Hadoop jobs - Go with Dataproc

dataproc

Google Cloud Dataproc == managed Spark and Hadoop service

B - Dataproc Lift&Shift of Apache Spark and Hadoop jobs

B is ok.