Solved: Dataproc variants - Pros / Cons and Usecases - Page 2

dheerajpanyam · 09-16-2024 06:07 AM

There are 3 Dataproc options

What are the pros/cons of each, I understand containers, serverless and IaaS fully but looking more from the operations perspective and also cost.

jaia

Hello,

Thank you for contacting Google Cloud Community!

Pros:

Full control: Offers the highest level of control over your cluster configuration.
Flexibility: Can be tailored to specific workloads and performance requirements.
Cost-effective for long-running clusters: Ideal for workloads that require consistent compute resources.

Cons:

Requires management: Requires more operational overhead to manage and scale clusters.
Higher upfront costs: Can have higher upfront costs due to provisioning and managing infrastructure.

Pros:

Managed Kubernetes: Leverages the managed Kubernetes platform for cluster management.
Container orchestration: Provides advanced container orchestration capabilities.
Hybrid workloads: Can run both batch and streaming workloads.

Cons:

Steeper learning curve: Requires familiarity with Kubernetes concepts and best practices.
Additional costs: May incur additional costs for GKE resources and services.

Pros:

Fully managed: No cluster management required.
Pay-as-you-go: Only pay for the resources used, making it cost-effective for intermittent workloads.
Scalability: Automatically scales to meet workload demands.

Cons:

Limited control: Offers less control over cluster configuration compared to Dataproc on GCE.
Potential for cold starts: May experience delays when starting new jobs after periods of inactivity.

Operational overhead: Dataproc on GCE requires the most operational overhead, while Dataproc Serverless requires the least.
Cost: Dataproc Serverless is generally the most cost-effective option for intermittent workloads, while Dataproc on GCE can be more cost-effective for long-running clusters.
Workload requirements: Consider the specific requirements of your workloads, such as batch processing, streaming, or machine learning, to determine the most suitable option.

Regards,

Jai Ade