Migration Services
Codimite migrates on-premises Hadoop, Spark, and Hive workloads to Google Cloud Dataproc. Reduce cluster administration, scale data processing, and modernize your big data platform through a controlled migration approach.
Modernize Big Data
Reduce time spent provisioning, configuring, updating, monitoring, and scaling Hadoop and Spark clusters.
Use autoscaling policies to adjust cluster resources based on workload requirements.
Run existing or modernized Spark workloads using managed clusters or serverless Spark environments.
Move persistent HDFS data to Cloud Storage and process it using temporary or independently scalable compute resources.
Continue using supported tools such as Spark, Hadoop, Hive, and Pig on managed cluster deployments.
Integrate workloads with BigQuery, Cloud Storage, Bigtable, Cloud Logging, and Cloud Monitoring.
Migration Process
Review Hadoop clusters, HDFS data, Spark and MapReduce jobs, Hive tables, dependencies, resource usage, security, and operational costs.
Define the target Google Cloud architecture, storage model, Dataproc deployment option, networking, IAM, governance, and orchestration approach.
Migrate a representative dataset and selected processing jobs to validate compatibility, performance, and cost assumptions.
Transfer HDFS data to Cloud Storage, update storage paths, migrate Spark or Hadoop jobs, and rebuild required pipelines and workflows.
Compare outputs, test performance and data quality, validate monitoring and security, and complete a phased production transition.
Why Codimite
As a Google Cloud Partner, Codimite combines cloud architecture, data engineering, application modernization, AI, security, and DevOps expertise to support end-to-end big data migrations.
Talk to a Data Migration ExpertBig Data Environment Assessment. We evaluate clusters, workloads, datasets, dependencies, performance, security, and administrative overhead.
Dataproc Architecture Design. We determine whether managed clusters, serverless Spark, or a hybrid approach best fits each workload.
HDFS Data Migration. We plan and execute secure data transfers from HDFS to Cloud Storage while validating completeness and integrity.
Spark and Hadoop Workload Migration. We migrate and optimize Spark, Hadoop, Hive, batch-processing, and related data workloads.
Security and Governance. We incorporate IAM, encryption, network controls, logging, monitoring, data access, and retention requirements.
End-to-End Support. Codimite supports assessment, architecture, migration, testing, deployment, optimization, documentation, and knowledge transfer.
Comparison
| Comparison Area | On-Premises Hadoop | Dataproc Advantage |
|---|---|---|
| Cluster management | Internal teams provision and maintain cluster infrastructure | ✓ Managed cluster and serverless deployment options |
| Capacity planning | Hardware must be sized and purchased in advance | ✓ Create, resize, autoscale, or remove resources as needed |
| Storage | Data is commonly stored within HDFS clusters | ✓ Use Cloud Storage independently from processing clusters |
| Scaling | Requires available physical or virtual cluster capacity | ✓ Autoscaling adjusts worker resources based on workload demand |
| Idle resources | Long-running clusters may remain provisioned between jobs | ✓ Use temporary clusters or serverless Spark for suitable workloads |
| Software management | Teams maintain Hadoop ecosystem components and configurations | ✓ Managed environments provide supported open-source components |
| Workload tools | Spark, Hadoop, Hive, Pig, and related technologies | ✓ Continue using familiar tools on managed clusters |
| Monitoring | Requires separate monitoring and logging architecture | ✓ Integrates with Cloud Logging and Cloud Monitoring |
| Data integration | Data may remain isolated within HDFS environments | ✓ Connect with Cloud Storage, BigQuery, Bigtable, and other services |
FAQs
Many existing workloads can be migrated with limited redevelopment because managed clusters support familiar Spark, Hadoop, Hive, and Pig tools. Code, dependencies, storage paths, and configurations still need to be assessed.
HDFS data can be transferred to Cloud Storage using Storage Transfer Service or compatible migration tools. Jobs can then access the data through the Cloud Storage connector.
Managed clusters suit workloads requiring Hadoop ecosystem components, customized cluster environments, or greater infrastructure control. Serverless Spark suits supported Spark workloads that do not require organizations to provision and manage clusters.
Yes. Managed cluster deployments support Apache Hive. Hive tables, metadata, storage locations, dependencies, and query compatibility must be assessed during migration.
It may create cost-optimization opportunities through autoscaling, temporary clusters, serverless execution, and separate storage and compute. Actual results depend on workload design, processing time, resource sizing, storage, and network usage.
The timeline depends on data volume, cluster size, number of jobs, Hive metadata, dependencies, security requirements, performance expectations, and the amount of modernization required.
Identify which data, Spark jobs, Hadoop workloads, and pipelines should be migrated or modernized through a focused assessment.
Talk to a Data Migration Expert
