Migration Services
Spark to Dataproc Migration Services
Move Spark jobs, data, libraries, and processing workflows to Dataproc through a structured migration designed to improve scalability, reliability, and operational efficiency.
Migration Services
Move Spark jobs, data, libraries, and processing workflows to Dataproc through a structured migration designed to improve scalability, reliability, and operational efficiency.
Why Dataproc?
Reduce the effort required to provision, configure, patch, monitor, and maintain self-managed Spark clusters.
Run Spark workloads using managed Dataproc clusters or Serverless for Apache Spark based on workload requirements.
Scale compute resources around workload demand instead of maintaining fixed cluster capacity.
Connect Spark workloads with Cloud Storage, BigQuery, Cloud Composer, Vertex AI, and other Google Cloud services.
How We Migrate
Review Spark jobs, runtime versions, libraries, data sources, clusters, schedules, security, performance, and workload dependencies.
Select Dataproc clusters or Serverless for Apache Spark and define networking, storage, security, orchestration, and migration waves.
Move Spark code, libraries, configuration, and data connections while updating platform-specific paths, dependencies, and integrations.
Compare source and target job outputs, processing time, resource usage, data quality, failure handling, and workload performance.
Transition production jobs and optimize cluster sizing, autoscaling, Spark configuration, scheduling, storage access, and cost controls.
Why Codimite?
Codimite combines Google Cloud expertise, Spark engineering, workload modernization, and structured validation to support the complete migration journey.
Start Your MigrationGoogle Cloud Expertise. We build the target environment using Dataproc and the wider Google Cloud data and analytics ecosystem.
Workload-Led Planning. We migrate related Spark jobs, libraries, data sources, schedules, security controls, and monitoring together.
Structured Validation. We verify job outputs, data quality, processing performance, reliability, and operational behavior before cutover.
Flexible Migration. We select managed clusters, Serverless for Apache Spark, or a hybrid model based on workload requirements.
Platform Comparison
Both environments run Apache Spark workloads, but Dataproc provides a more flexible, managed approach to infrastructure, scalability, integration, and operations.
| Area | Self-Managed Apache Spark | Dataproc |
|---|---|---|
| Platform model | Open-source processing framework deployed on self-managed infrastructure | ✓ Managed Apache Spark and Hadoop service on Google Cloud |
| Infrastructure | Requires teams to provision and maintain clusters and supporting infrastructure | ✓ Cluster infrastructure, provisioning, and service integration managed through Google Cloud |
| Deployment options | Commonly runs on physical servers, virtual machines, or self-managed Kubernetes | ✓ Managed Dataproc clusters or Serverless for Apache Spark |
| Scaling | Requires manual scaling or custom autoscaling configuration | ✓ Supports managed autoscaling and serverless resource allocation |
| Cluster lifecycle | Teams manage cluster creation, availability, upgrades, and retirement | ✓ Create short-lived or persistent clusters and remove them when no longer required |
| Spark compatibility | Full control over Spark versions, packages, and cluster configuration | ✓ Runs Apache Spark with configurable properties, libraries, and custom containers where supported |
| Workload types | Supports batch, streaming, SQL, and machine-learning workloads | ✓ Supports Spark batch, SQL, streaming, interactive, and machine-learning workloads |
| Data storage | Often relies on HDFS, local storage, or external data systems | ✓ Native integration with Cloud Storage, BigQuery, BigLake, and other Google Cloud services |
| Orchestration | Requires external scheduling and workflow tools | ✓ Integrates with Cloud Composer, Workflows, and Google Cloud scheduling services |
| Monitoring | Requires teams to configure and maintain logging and monitoring tools | ✓ Integrated with Cloud Logging, Cloud Monitoring, Spark History Server, and managed diagnostics |
| Security | Security depends on the underlying infrastructure and custom configuration | ✓ Integrates with Google Cloud IAM, private networking, CMEK, VPC Service Controls, and audit logging |
| Pricing | Includes infrastructure, administration, idle capacity, and operational overhead | ✓ Usage-based compute with options for temporary clusters and serverless execution |
| Operations | Teams manage provisioning, patching, scaling, failures, and upgrades | ✓ Google Cloud manages service infrastructure while teams focus on Spark workloads |
FAQs
We can migrate PySpark, Scala, Java, Spark SQL, and Spark R jobs, along with libraries, configurations, data connections, schedules, monitoring, and operational workflows.
Some jobs may run with limited changes, but filesystem paths, libraries, runtime versions, security, integrations, and platform-specific configurations often require updates.
The decision depends on workload frequency, startup requirements, customization, isolation, performance, and cost. Persistent or temporary clusters provide greater environment control, while serverless execution reduces cluster-management work.
Serverless for Apache Spark supports PySpark, Spark SQL, Spark R, and Java or Scala Spark batch workloads, along with interactive sessions.
Data can be transferred from HDFS, local storage, or other systems to Cloud Storage, BigQuery, or another suitable destination based on format, volume, access patterns, and performance requirements.
Many libraries can be reused, but compatibility with the selected Spark runtime, Java version, Python environment, and Google Cloud services must be tested.
Yes. Cloud Composer can schedule and orchestrate Dataproc cluster jobs and Serverless for Apache Spark batch workloads.
We compare source and target outputs, row counts, aggregates, processing time, resource usage, failure handling, and downstream results.
Not necessarily. Cost depends on infrastructure utilization, idle capacity, workload duration, administration, storage, and the selected Dataproc operating model.
Yes. Existing Spark workloads can remain active while selected jobs are migrated, tested, and transitioned in phases.
Assess your Spark jobs, data, libraries, runtime dependencies, and operational requirements to build a practical Dataproc migration roadmap.
Start Your Migration
