Cloud Dataproc

A faster, easier, more cost-effective way to run Apache Spark and Apache Hadoop

Try It Free

Cloud-native Apache Hadoop & Apache Spark

Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead, and you pay only for the resources you use (with per-second billing). Cloud Dataproc also easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning.

Managed Hadoop and Spark

Fast & Scalable Data Processing

Create Cloud Dataproc clusters quickly and resize them at any time—from three to hundreds of nodes—so you don't have to worry about your data pipelines outgrowing your clusters. With each cluster action taking less than 90 seconds on average, you have more time to focus on insights, with less time lost to infrastructure.

Fast and Scalable Data Processing

Affordable Pricing

Adopting Google Cloud Platform pricing principles, Cloud Dataproc has a low cost and an easy to understand price structure, based on actual use, measured by the second. Also, Cloud Dataproc clusters can include lower-cost preemptible instances, giving you powerful clusters at an even lower total cost.

Affordable Pricing

Open Source Ecosystem

The Spark and Hadoop ecosystem provides tools, libraries, and documentation that you can leverage with Cloud Dataproc. By offering frequently updated and native versions of Spark, Hadoop, Pig, and Hive, you can get started without needing to learn new tools or APIs, and you can move existing projects or ETL pipelines without redevelopment.

Open Source Ecosystem

Cloud Dataproc Features

Google Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that is fast, easy to use, and low cost.

Automated Cluster Management
Managed deployment, logging, and monitoring let you focus on your data, not on your cluster. Your clusters will be stable, scalable, and speedy.
Resizable Clusters
Clusters can be created and scaled quickly with a variety of virtual machine types, disk sizes, number of nodes, and networking options.
Built-in integration with Cloud Storage, BigQuery, Bigtable, Stackdriver Logging, and Stackdriver Monitoring, 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 with multiple master nodes and set jobs to restart on failure to ensure your clusters and jobs are highly available.
Developer Tools
Multiple ways to manage a cluster, including an easy-to-use Web UI, the Google 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.
Automatic or Manual Configuration
Cloud Dataproc automatically configures hardware and software on clusters for you while also allowing for manual control.
Flexible Virtual Machines
Clusters can use custom machine types and preemptible virtual machines so they are the perfect size for your needs.

Cloud Dataflow vs. Cloud Dataproc: Which should you use?

Cloud Dataproc and Cloud Dataflow can both be used for data processing, and there’s overlap in their batch and streaming capabilities. How do you decide which product is a better fit for your environment?
Dataproc vs Dataflow

Cloud Dataproc

Cloud Dataproc is good for environments dependent on specific components of the Apache big data ecosystem:

  • Tools/packages
  • Pipelines
  • Skill sets of existing resources

Cloud Dataflow

Cloud Dataflow is typically the preferred option for greenfield environments:

  • Less operational overhead
  • Unified approach to development of batch or streaming pipelines
  • Uses Apache Beam
  • Supports pipeline portability across Cloud Dataflow, Apache Spark, and Apache Flink as runtimes

Recommended Workloads

Stream processing (ETL)
Batch processing (ETL)
Iterative processing and notebooks
Machine learning with Spark ML
Preprocessing for machine learning (with Cloud ML Engine)

Cloud Dataproc Pricing

Cloud Dataproc incurs a small incremental fee per virtual CPU in the Compute Engine instances used in your cluster1.

Iowa (us-central1) Oregon (us-west1) Northern Virginia (us-east4) South Carolina (us-east1) Montréal (northamerica-northeast1) São Paulo (southamerica-east1) Belgium (europe-west1) London (europe-west2) Netherlands (europe-west4) Zürich (europe-west6) Frankfurt (europe-west3) Sydney (australia-southeast1) Mumbai (asia-south1) Hong Kong (asia-east2) Taiwan (asia-east1) Tokyo (asia-northeast1) Osaka (asia-northeast2)
Machine Type Price
Standard Machines
1-64 Virtual CPUs
High Memory Machines
2-64 Virtual CPUs
High CPU Machines
2-64 Virtual CPUs
Custom Machines
Based on vCPU and memory usage
If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

1 Google Cloud Dataproc incurs a small incremental fee per virtual CPU in the Compute Engine instances used in your cluster while the cluster is operational. Additional resources used by Cloud Dataproc, such as a Compute Engine network, BigQuery, Cloud Bigtable, and others, are billed as they are consumed. For detailed pricing information, please view the pricing guide.

Send feedback about...

Cloud Dataproc