Google Cloud Dataproc enables fast provisioning of Apache Spark and Hadoop clusters for big data workloads.
What is Dataproc?
Dataproc is a fully managed service for Apache Spark, Hadoop, Presto, and other open-source tools. Clusters start in about 90 seconds and are billed per-second. With Dataproc Serverless, Spark jobs can run without any cluster management.
Core Features
- Fast cluster provisioning: Clusters ready in 90 seconds with pre-configured images
- Dataproc Serverless: Spark jobs without cluster management with automatic scaling
- Native GCP integration: Direct connection to BigQuery, Cloud Storage, and Vertex AI
- Autoscaling: Automatic adjustment of cluster size based on workload
- Spot VM support: Up to 80% cost savings through preemptible instances
Typical Use Cases
ETL and Data Processing
Migrate existing Hadoop or Spark ETL pipelines to the cloud with minimal code changes. Dataproc supports all common Spark APIs and libraries.
Data Lake Analytics
Analyze large amounts of data in Cloud Storage with Spark SQL or Presto. Direct integration with BigQuery enables hybrid analytics across data lake and data warehouse.
Machine Learning with Spark MLlib
Train ML models on large datasets with Spark MLlib. Integration with Vertex AI for model deployment and monitoring.
Benefits
- Open-source compatibility: Run unmodified Spark, Hadoop, and Presto workloads
- Cost efficiency: Per-second billing and Spot VMs for temporary workloads
- Fast migration: Migrate existing on-premise workloads without refactoring
- Flexible options: Choose between cluster-based and serverless depending on requirements
Integration with innFactory
As a Google Cloud Partner, innFactory supports you with Dataproc: migration from on-premise Hadoop clusters, optimization of existing Spark jobs, architecture of data lake analytics solutions, and cost optimization through proper cluster configuration.
Available Tiers & Options
Dataproc on Compute Engine
- Full control over cluster configuration
- Spot VMs for cost optimization
- Autoscaling support
- Cluster management required
Dataproc Serverless
- No cluster management
- Automatic resource scaling
- Fastest startup
- Fewer configuration options
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What's the difference between Dataproc and Dataflow?
Dataproc is optimized for existing Spark/Hadoop workloads, while Dataflow is a fully serverless service for Apache Beam pipelines. Dataproc is better suited for migrations from on-premise Hadoop clusters.
How fast is a Dataproc cluster ready?
Dataproc clusters start in about 90 seconds. Dataproc Serverless eliminates startup time for Spark jobs completely.
Can I run existing Spark jobs without changes?
Yes, Dataproc is fully compatible with Apache Spark, Hadoop, Hive, Pig, and Presto. Existing jobs can be migrated without code changes.
How is Dataproc billed?
Dataproc charges per-second based on the Compute Engine VMs used plus a small Dataproc surcharge. Spot VMs can reduce costs by up to 80%.
Is Dataproc GDPR-compliant?
Yes, Dataproc is available in EU regions and meets all GDPR requirements. Data can be encrypted with Customer-Managed Encryption Keys (CMEK).
