Migration Services
Cloudera to Dataproc Migration Services
Move Cloudera data and processing workloads to Dataproc and purpose-built Google Cloud services through a structured migration designed to simplify operations and improve scalability.
Migration Services
Move Cloudera data and processing workloads to Dataproc and purpose-built Google Cloud services through a structured migration designed to simplify operations and improve scalability.
Why Dataproc?
Reduce the effort required to provision, maintain, patch, scale, and monitor Hadoop and Spark clusters.
Move HDFS data to Cloud Storage and scale Dataproc processing resources separately according to workload demand.
Use persistent clusters, temporary clusters, or serverless Spark execution based on workload duration, frequency, and control requirements.
Connect processing workloads with BigQuery, BigLake, Dataflow, Looker, Vertex AI, and the wider Google Cloud ecosystem.
How We Migrate
Review Cloudera services, cluster versions, HDFS data, Spark and Hive jobs, security policies, metadata, integrations, and operational dependencies.
Define the Google Cloud architecture for storage, processing, analytics, governance, networking, security, and workload orchestration.
Move HDFS data to Cloud Storage and adapt Spark, Hive, and compatible Hadoop workloads for Dataproc or other suitable Google Cloud services.
Compare data, processing outputs, metadata, access controls, schedules, downstream applications, and workload performance.
Transition production workloads and optimize cluster lifecycle, autoscaling, storage access, orchestration, monitoring, and cost controls.
Why Codimite?
Codimite combines Google Cloud architecture, data engineering, workload migration, and governance planning to modernize the complete Cloudera environment.
Start Your MigrationCapability-Based Architecture. We map Cloudera storage, processing, SQL, governance, and machine-learning capabilities to appropriate Google Cloud services.
Data and Workload Migration. We move HDFS data together with the Spark, Hive, reporting, and application workloads that depend on it.
Security and Governance Planning. We redesign Cloudera identity, policy, metadata, and access controls using Google Cloud security and governance capabilities.
Controlled Platform Transition. We reduce risk by migrating data domains and workload groups through phased pilots and production waves.
Platform Comparison
Cloudera provides a broad data-platform environment, while Dataproc delivers managed Spark and Hadoop processing as part of a modular Google Cloud architecture.
| Area | Cloudera | Dataproc and Google Cloud |
|---|---|---|
| Platform model | Integrated enterprise data platform covering storage, processing, governance, and analytics | Managed Spark and Hadoop processing combined with purpose-built Google Cloud services |
| Infrastructure | Requires cluster infrastructure and platform administration depending on deployment | Google Cloud manages Dataproc service infrastructure and simplifies cluster provisioning |
| Storage | Commonly uses HDFS or cloud object storage depending on the environment | Cloud Storage provides durable, scalable storage independent from compute |
| Processing | Spark, Hive, MapReduce, and related engines run within the Cloudera platform | Dataproc supports managed Spark, Hadoop, Hive, and compatible open-source workloads |
| Scaling | Scaling depends on cluster size, node configuration, and deployment architecture | Supports autoscaling, temporary clusters, and serverless Spark execution |
| Cluster lifecycle | Clusters may remain active to support storage and ongoing processing | Create persistent or short-lived clusters according to workload needs |
| SQL analytics | Uses Hive, Impala, and related Cloudera analytical services | BigQuery provides fully managed, serverless SQL analytics for suitable workloads |
| Data access | Data access is managed through Cloudera platform services and security tools | BigLake and BigQuery provide governed access to data stored across supported systems |
| Security | May use Kerberos, Apache Ranger, Apache Sentry, and Cloudera security controls | Uses Google Cloud IAM, private networking, encryption, audit logs, and data-policy controls |
| Governance | Uses Cloudera metadata, lineage, catalog, and governance capabilities | Integrates with Google Cloud metadata, catalog, lineage, and governance services |
| Orchestration | Uses Cloudera tools or external workflow platforms | Integrates with Cloud Composer, Workflows, and Google Cloud scheduling services |
| Machine learning | Supports Cloudera machine-learning tools and connected frameworks | Connects processing and enterprise data with Vertex AI and Gemini |
| Monitoring | Uses Cloudera management and monitoring tools | Integrates with Cloud Monitoring, Cloud Logging, diagnostics, and Spark History Server |
| Operations | Teams manage platform services, upgrades, capacity, and cluster health | Google manages service infrastructure while teams focus on data and processing workloads |
FAQs
Codimite can migrate HDFS data, Spark jobs, Hive workloads, MapReduce applications, metadata, schedules, security policies, monitoring processes, and connected analytics workflows.
No. Dataproc primarily provides managed Spark and Hadoop processing. Cloudera storage, SQL analytics, governance, metadata, and machine learning capabilities may map to Cloud Storage, BigQuery, BigLake, Vertex AI, and other Google Cloud services.
HDFS data commonly moves to Cloud Storage, allowing storage to scale independently from Dataproc compute. Some datasets may also move to BigQuery or BigLake based on analytical and governance requirements.
Many compatible workloads can be adapted for Dataproc. Runtime versions, libraries, filesystem paths, custom functions, security settings, and Cloudera-specific dependencies must be reviewed and tested.
Apache Ranger or Sentry policies can be mapped to Google Cloud IAM, dataset permissions, policy tags, and row-level security. Metadata, ownership, and lineage are mapped to the selected Google Cloud governance architecture.
We compare data counts, business totals, Spark and Hive outputs, schedules, permissions, downstream results, processing performance, and operational behavior before production cutover.
Assess your Cloudera data, processing workloads, metadata, security, and dependencies to build a practical Google Cloud migration roadmap.
Start Your Migration
