Dataproc is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks. Use Dataproc for data lake modernization, ETL, and secure data science, at scale, integrated with Google Cloud, at a fraction of the cost.
Open: Run open source data analytics at scale, with enterprise grade security
Flexible: Use serverless, or manage clusters on Google Compute and Kubernetes
Secure: Configure advanced security such as Kerberos, Apache Ranger and Personal Authentication
Cost-effective: Realize 54% lower TCO compared to on-prem data lakes with per-second pricing
Whether you need VMs or Kubernetes, extra memory for Presto, or even GPUs, Dataproc can help accelerate your data and analytics processing through on-demand purpose-built or serverless environments.
Serverless deployment, logging, and monitoring let you focus on your data and analytics, not on your infrastructure. Reduce TCO of Apache Spark management by up to 54%. Enable data scientists and engineers to build and train models 5X faster, compared to traditional notebooks, through integration with Vertex AI Workbench. The Dataproc Jobs API makes it easy to incorporate big data processing into custom applications, while Dataproc Metastore eliminates the need to run your own Hive metastore or catalog service.
Build your Apache Spark jobs using Dataproc on Kubernetes so you can use Dataproc with Google Kubernetes Engine (GKE) to provide job portability and isolation.
When you create a Dataproc cluster, you can enable Hadoop Secure Mode via Kerberos by adding a Security Configuration. Additionally, some of the most commonly used Google Cloud-specific security features used with Dataproc include default at-rest encryption, OS Login, VPC Service Controls, and customer-managed encryption keys (CMEK).
Dataproc lets you take the open source tools, algorithms, and programming languages that you use today, but makes it easy to apply them on cloud-scale datasets. At the same time, Dataproc has out-of-the-box integration with the rest of the Google Cloud analytics, database, and AI ecosystem. Data scientists and engineers can quickly access data and build data applications connecting Dataproc to BigQuery, Vertex AI, Cloud Spanner, Pub/Sub, or Data Fusion.
Enterprises are migrating their existing on-premises Apache Hadoop and Spark clusters over to Dataproc to manage costs and unlock the power of elastic scale. With Dataproc, enterprises get a fully managed, purpose-built cluster that can autoscale to support any data or analytics processing job.
Create your ideal data science environment by spinning up a purpose-built Dataproc cluster. Integrate open source software like Apache Spark, NVIDIA RAPIDS, and Jupyter notebooks with Google Cloud AI services and GPUs to help accelerate your machine learning and AI development.
Deploy Spark applications and pipelines that autoscale without any manual infrastructure provisioning or tuning.
Create and scale clusters quickly with various virtual machine types, disk sizes, number of nodes, and networking options.
Dataproc autoscaling provides a mechanism for automating cluster resource management and enables automatic addition and subtraction of cluster workers (nodes).
Built-in integration with Cloud Storage, BigQuery, Dataplex, Vertex AI, Composer, Cloud Bigtable, Cloud Logging, and Cloud Monitoring, giving you a more complete and robust data platform.
Image versioning allows you to switch between different versions of Apache Spark, Apache Hadoop, and other tools.
|Cluster scheduled deletion|
To help avoid incurring charges for an inactive cluster, you can use 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 specified time period.
|Automatic or manual configuration|
Dataproc automatically configures hardware and software but also gives you manual control.
Multiple ways to manage a cluster, including an easy-to-use web UI, the Cloud SDK, RESTful APIs, and SSH access.
Run initialization actions to install or customize the settings and libraries you need when your cluster is created.
Use optional components to install and configure additional components on the cluster. Optional components are integrated with Dataproc components and offer fully configured environments for Zeppelin, Presto, and other open source software components related to the Apache Hadoop and Apache Spark ecosystem.
|Custom containers and images|
|Flexible virtual machines|
|Component Gateway and notebook access|
Dataproc Component Gateway enables secure, one-click access to Dataproc default and optional component web interfaces running on the cluster.
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.
|Automated policy management|
Standardize security, cost, and infrastructure policies across a fleet of clusters. You can create policies for resource management, security, or network at a project level. You can also make it easy for users to use the correct images, components, metastore, and other peripheral services, enabling you to manage your fleet of clusters and serverless Spark policies in the future.
Dataproc recommended alerts allow customers to adjust the thresholds for the pre-configured alerts to get alerts on idle, runaway clusters, jobs, overutilized clusters and more. Customers can further customize these alerts and even create advanced cluster and job management capabilities. These capabilities allow customers to manage their fleet at scale.
Fully managed, highly available Hive Metastore (HMS) with fine-grained access control and integration with BigQuery metastore, Dataplex, and Data Catalog.
Dataproc pricing is based on the number of vCPU and the duration of time that they run. While pricing shows hourly rate, we charge down to the second, so you only pay for what you use.
Ex: 6 clusters (1 main + 5 workers) of 4 CPUs each ran for 2 hours would cost $.48. Dataproc charge = # of vCPUs * hours * Dataproc price = 24 * 2 * $0.01 = $0.48
Please see pricing page for details.
Dataproc integrates with key partners to complement your existing investments and skill sets.