Google Cloud Dataproc

Process big data workloads without the operational burden of managing clusters. Dataproc provisions Spark and Hadoop clusters in seconds with built-in integration to other Google Cloud services like BigQuery and Cloud Storage.

Highlights:
Quick cluster creation (90 seconds average)
Integrated with BigQuery, Cloud Storage, and Bigtable
Custom machine types for cost optimization
Automated cluster scaling based on workload
Built-in monitoring and logging

More Details

About this Item

Google Cloud Dataproc

What is Google Cloud Dataproc?

Google Cloud Dataproc is a fully managed cloud service for running Apache Spark and Apache Hadoop clusters. It simplifies the management of big data processing workloads with integrated tools for data processing and analytics.

Key Features

  • Quick cluster creation (90 seconds on average)
  • Integrated with BigQuery, Cloud Storage, and Bigtable
  • Custom machine types for cost optimization
  • Automated cluster scaling based on workload
  • Built-in monitoring and logging

Use Cases

  • Big data processing and analysis
  • ETL (Extract, Transform, Load) operations
  • Machine learning with Spark MLlib
  • Interactive data explorationLog processing and analysis

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App Type and Industry Use Cases

Take a look at the diverse range of application types and industries that can leverage this solution. Understanding these potential uses can help identify how this can integrate into different business models and digital solutions across various sectors.

Tech Stack Compatibility

We've outlined the compatible tech stacks for this solution, encompassing various development areas like frontend, backend, and database, alongside specific stack environments. This provides you with the crucial information needed for seamless integration into your preferred frameworks and programming environments.

Google Cloud Dataproc
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Pricing Tiers

Standard
Fully managed cloud service for running Apache Spark and Hadoop clusters with on-demand provisioning. Ideal for data processing, ETL, and analytics.
$0.01 per vCPU per hour + underlying VM costs
Cluster creation in 90 secs
Integrated monitoring
Component versioning
Preemptible VMs option
Manual scaling
Autoscaling
Enhanced Dataproc with automatic scaling based on workload demands to optimize resource utilization and costs for variable processing needs.
$0.01 per vCPU per hour + underlying VM costs
Auto-scaling clusters
Graceful decommissioning
Monitoring integration
Policy-based scaling
Workload-aware scaling
Serverless
Spark serverless offering that eliminates cluster management entirely, allowing users to run Spark jobs without provisioning or managing clusters.
Pay only for resources used during job execution
No cluster management
Job-based execution
Automatic resource allocation
Simplified operations
Built-in job monitoring

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