Step Snap 1: [Dataproc Cluster]

Dataproc Cluster is a managed service offered by Google Cloud that enables users to quickly and easily launch and manage Hadoop, Spark, and Hive clusters in the cloud. Dataproc makes large-scale data analysis and processing tasks more efficient by combining the elasticity of Google Cloud with the powerful capabilities of Hadoop/Spark.

Why is it called Dataproc?

"Dataproc" is composed of two parts:

• Data: Referring to the data that Dataproc processes and analyzes.
• Proc: Derived from "Processing," meaning Dataproc is a service for data processing.

So, "Dataproc" means "data processing," and it is specifically designed for handling large datasets, particularly when distributed computing frameworks like Hadoop and Spark are required.

Purpose of Dataproc Cluster

Dataproc Cluster allows users to easily create, manage, and run distributed computing clusters for tasks such as data processing, machine learning, and data analysis. Users can create clusters via simplified commands, APIs, or the Google Cloud Console, and scale them as needed.

These clusters are commonly used for:

• Large-scale data analytics
• Data ETL (Extract, Transform, Load)
• Data mining and machine learning

In short, Dataproc Cluster is a convenient tool on Google Cloud that helps users manage and run big data tasks efficiently.

Step Snap 2: [Dataproc Cluster Deployment: Compute Engine vs GKE]

When creating a Dataproc cluster, you can choose to deploy it on Compute Engine (GCE) or GKE, with the core differences lying in their underlying infrastructure and resource management approaches:

1. Underlying Infrastructure:
   • Compute Engine: Based on virtual machines (VMs), Dataproc runs Hadoop/Spark and other big data services on a group of GCE VMs.
   • GKE: Based on the Kubernetes container orchestration platform, Dataproc deploys jobs as containers in a GKE cluster.
2. Resource Management and Elasticity:
   • Compute Engine: Resource allocation through VMs, scaling primarily involves adding or reducing the number or size of VMs; typical scenarios include needing a complete Hadoop/Spark cluster or being familiar with VM management.
   • GKE: Manages resources through Kubernetes scheduling and containerized deployment, with pod elasticity being more fine-grained than VM scaling; suitable for existing Kubernetes/GKE workloads or those wanting more flexible scheduling and resource sharing.