- Automated Cluster Management
- Managed deployment, logging, and monitoring let you focus on your data, not on your cluster. Cloud Dataproc clusters are stable, scalable, and speedy.
- Resizable Clusters
- Create and scale clusters quickly with various virtual machine types, disk sizes, number of nodes, and networking options.
- Autoscaling Clusters
- Cloud Dataproc Autoscaling provides a mechanism for automating cluster resource management, and enables automatic addition and subtraction of cluster workers (nodes).
- Cloud Integrated
- Built-in integration with Cloud Storage, BigQuery, Cloud Bigtable,Stackdriver Logging,Stackdriver Monitoring ,and AI Hub ,giving you a complete and robust data platform.
- Image versioning allows you to switch between different versions of Apache Spark, Apache Hadoop, and other tools.
- Highly available
- Run clusters in high availability mode with multiple master nodes, and set jobs to restart on failure to ensure your clusters and jobs are highly available.
- Enterprise Security
- When you create a Cloud Dataproc cluster, you can enable Hadoop Secure Mode via Kerberos by adding a Security Configuration. Also,GCP and Cloud Dataproc offer additional security features that help protect your data. Some of the most commonly used GCP-specific security features used with Cloud Dataproc include default at-rest encryption, OS Login, VPC Service Controls, and Customer Managed Encryption Keys (CMEK)
- Cluster Scheduled Deletion
- To help avoid incurring charges for an inactive cluster, you can use Cloud Dataproc's scheduled deletion ,which provides options to delete a cluster after a specified cluster idle period, at a specified future time, or after a specfied time period.
- Automatic or Manual Configuration
- Cloud Dataproc automatically configures hardware and software, but also gives you manual control.
- Developer Tools
- Multiple ways to manage a cluster, including an easy-to-use web UI, the Cloud SDK, RESTful APIs, and SSH access.
- Initialization Actions
- Run initialization actions to install or customize the settings and libraries you need when your cluster is created.
- Optional Components
- Use optional components to install and configure additional components on the cluster. Optional components are integrated with Cloud Dataproc components, and offer fully configured environments for Zeppelin, Druid, Presto, and other open source software components related to the Apache Hadoop and Apache Spark ecosystem.
- Custom Images
- Cloud Dataproc clusters can be provisioned with a custom image that includes your pre-installed Linux operating system packages.
- Flexible Virtual Machines
- Clusters can use custom machine types and preemptible virtual machines to make them the perfect size for your needs.
- Component Gateway and Notebook Access
- Cloud Dataproc Component Gateway enables secure, one-click access to Cloud Dataproc default and optional component web interfaces running on the cluster.
- Workflow Templates
- Cloud Dataproc workflow templates provide a flexible and easy-to-use mechanism for managing and executing workflows. A Workflow Template is a reusable workflow configuration that defines a graph of jobs with information on where to run those jobs.
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Cloud AI products comply with the SLA policies listed here. They may offer different latency or availability guarantees from other Google Cloud services.