Bifrost migration toolkit

Exit Cloudera. Modernize Hadoop. Without betting the business.

Migrate off Cloudera licensing with Bifrost, Ilum's proven toolkit: an in-place weekend cutover, or a phased move to a Kubernetes lakehouse. Zero data loss, reversible until you finalize.

$500K-2Mannual Cloudera licensing eliminated
6-8hovernight downtime for an in-place weekend cutover
60-75%typical infrastructure cost reduction
Zerodata loss, reversible until you finalize
Why teams choose Bifrost

A repeatable path off Cloudera, not a one-off project

Cloudera licensing keeps climbing while the estate ages. Bifrost gives platform teams an auditable, reversible way to leave, without the big-bang risk.

Eliminate Cloudera licensing

Replatform Cloudera CDP onto license-free open-source Hadoop, or modernize to Ilum on Kubernetes. Cut $500K-2M in annual Cloudera license fees while keeping your data and workloads intact.

Weekend cutover, fully reversible

An in-place license exit can complete in a single weekend window, with roughly 6-8 hours of overnight downtime. Every step stays reversible through a multi-day soak period, so if anything misbehaves you roll back.

Zero data loss, proven framework

Bifrost automates discovery, configuration translation, data movement, and validation with go/no-go gates at every phase. Data directories are never touched in place, and row-count and value-level checks must pass before cutover.

The proven weekend migration

Your Cloudera exit, in one maintenance window

With Bifrost, a Cloudera CDP cluster is replatformed onto license-free open-source Hadoop in a single weekend. Core services are offline for roughly 6-8 hours overnight; the rest of the weekend runs validation and benchmarking.

For 100+ node estates that must keep HDFS online, the shrink-and-grow strategy migrates nodes in batches with no full-cluster downtime, typically completing in a few days.

See the full step-by-step Classic procedure
Sat 20:00

Cutover begins. Packages are swapped in place. Data directories are never touched.

6-8h

Overnight downtime while HDFS is upgraded in parallel across all nodes.

Sunday

Automated validation and benchmarks: fsck, block-count parity, TeraSort, TestDFSIO, smoke tests.

Monday

Production resumes on open-source Hadoop. Cloudera licensing is eliminated.

Soak

A multi-day rollback window stays open until you choose to finalize.

Three ways to leave Cloudera

Pick the path that fits your estate

Bifrost runs in-place replatforms and Kubernetes modernizations, independently or chained together. Every path is gated, auditable, and reversible until finalize.

Classic

Exit Cloudera in place
Cloudera CDP 7.xOpen-source Apache Hadoop

Replatform onto license-free open-source Hadoop on the same hardware. Removes Cloudera licensing without changing your architecture. Ideal when a move to Kubernetes is not yet on the table.

4-6 months per multi-cluster estate, weekend cutover per cluster

Modernize

Hadoop to a Kubernetes lakehouse
Open-source HadoopIlum on Kubernetes

Migrate table by table with near-zero downtime. HDFS moves to S3A object storage, Hive to Apache Iceberg and Trino, Oozie to Airflow, Ranger to Open Policy Agent.

2-5 months, fully phased

Direct

Cloudera straight to Kubernetes
Cloudera CDPIlum on Kubernetes

A single engagement that skips the intermediate open-source Hadoop step, combining Cloudera extraction with table-by-table modernization.

2-5 months

Sprint

Fast track
Escape Cloudera in 30 days
Cloudera CDPOpen-source Hadoop or Ilum

A compressed, all-hands engagement for teams that need off Cloudera now. The same gated, zero-data-loss, reversible-until-finalize Bifrost playbook, run on an accelerated schedule with a dedicated team. Best fit for well-scoped, single-cluster estates.

Full migration in 30 days
Zero-touch for app teams

Your data teams won't have to change a thing

During the Classic license exit, Bifrost preserves interfaces and paths. Jobs, pipelines, and dashboards keep running, so there is no forced code migration. That continuity is exactly what lets you modernize later, at your own pace.

Stays exactly the same

  • Spark and Hive jobs run unchanged
  • Oozie workflows keep scheduling
  • Application and HDFS paths stay valid through compatibility symlinks
  • SQL, BI dashboards, and reports keep working
  • The Kerberos and Ranger security model is preserved

Changes under the hood

  • Cloudera distribution becomes open-source Apache Hadoop
  • Cloudera licensing is eliminated
  • Management tooling moves to open-source and GitOps
  • Then, at your pace: HDFS to object storage, Oozie to Airflow, Hive to Trino
  • Consumers keep working via the dual-read bridge and table redirection

A strangler-style dual-read bridge keeps the legacy path live until each table is validated and redirected. That is what makes progressive modernization, such as moving Oozie to Airflow or Hive to Trino, possible without a big-bang rewrite.

No vendor lock-in

Leave Cloudera, and keep the freedom to leave again

Cloudera lock-in is more than licensing. It is the management plane, the support contract, the hardware cycle, and proprietary formats. Bifrost lands you on open foundations, so you are never trapped again.

Licensing
Per-node, perpetual fees that rise with the cluster
Open-source Apache Hadoop with no license fee
Control plane
Proprietary Cloudera Manager
Apache Ambari and GitOps, fully open
Support
Locked to a commercial support contract
Community support, with optional commercial backing
Hardware
Tied to refresh cycles and a fixed footprint
Cloud-elastic, scale on demand
Data formats
Proprietary formats and catalog
Apache Iceberg, Parquet, ORC, and S3 APIs

The exit path is not obstructed by proprietary data formats or catalog implementations, so you keep the option to leave and run the open-source components directly. Read our data sovereignty approach

EU DORANIS2HIPAASOC 2 Type IIISO/IEC 27001
How Bifrost runs your migration

A phased pipeline with go/no-go gates

Each phase ends in a decision verdict. Critical failures trigger automatic rollback. From discovery through cutover, every step stays reversible until you finalize.

01

Discover

Bifrost connects to Cloudera Manager and inventories hosts, roles, service configurations, security assets, and encryption zones into a version-controlled inventory.

02

Extract and validate-pre

PROCEED / WARN / ABORT

Translate source configuration to target equivalents, then run the full pre-flight checklist with explicit verdicts before any production change.

03

Weekend cutover

Back up rollback assets, stop services, swap packages in place (data directories untouched), deploy configs, and start services with the HDFS upgrade.

04

Validate-post and soak

Run row-count parity, data-diff, and benchmark checks (TeraSort, TestDFSIO, smoke tests). A multi-day soak keeps a full rollback option open.

05

Finalize

After a successful soak, finalize. Cloudera licensing is eliminated and the estate runs on open-source Hadoop or the Ilum lakehouse on Kubernetes.

What Bifrost migrates

Every layer mapped to a modern equivalent

Bifrost is explicit about automation ceilings: how much of each component it moves end to end, and what needs manual follow-up.

HDFS
S3A object storage
85-97% automated
YARN
Spark on Kubernetes
~95% automated
Hive Metastore
Iceberg REST catalog
70-90% automated
Hive / Impala
Trino
75-85% automated
OoOozie
Apache Airflow
40-55% automated
Ranger
OpOpen Policy Agent
60-70% automated
Atlas
OpenMetadata
60-75% automated
Architecture evolution

From monolithic clusters to a cloud-native lakehouse

Bifrost bridges the gap, preserving data integrity while moving the platform to elastic, vendor-independent infrastructure.

Traditional Hadoop

  • Monolithic cluster design
  • Manual scaling processes
  • Static resource allocation
  • Complex maintenance requirements

Limited scalability, high operational overhead, vendor lock-in, and manual configuration management, with expensive hardware and lengthy deployment cycles.

Kubernetes lakehouse

  • Microservices containerization
  • Auto-scaling capabilities
  • Dynamic resource optimization
  • Cloud-native integrations

Elastic scalability, cost optimization, vendor independence, and automated operations, with cloud-agnostic deployment and integrated monitoring.

Bifrost migration engine

  • Automated discovery and config translation
  • Resumable data movement
  • Gated validation (PROCEED, WARN, ABORT)
  • Reversible until finalize

Auditable, reproducible playbooks with row-count and value-level data-diff validation, full rollback assets, a weekend in-place cutover, and zero data loss.

The business case

Where the money goes when Cloudera leaves

Licensing is only the first line item. Support contracts and hardware refresh cycles fall away too.

Cloudera license elimination

$500K-2M annual licensing fees

Complete elimination of Cloudera licensing costs

100% reduction in software licensing expense

Support and maintenance

$200K-800K annual support contracts

Reduced dependency on vendor support with open-source alternatives

70-80% reduction in support costs

Hardware refresh avoidance

$1M-5M every 3-4 years

Cloud-native infrastructure with elastic scaling

60-90% reduction in hardware capital expenditure
Infrastructure cost$2M annual Hadoop cluster costs$700K with Kubernetes optimizationTypical 60-65% reduction
Batch processing4-hour batch processing windows45-minute processing with auto-scalingUp to 80% faster
Operations effort20 FTE for cluster management5 FTE with automated operationsPotential 50-75% less overhead
How it compares

Bifrost vs do-it-yourself vs staying on Cloudera

Staying is comfortable until the next renewal. A do-it-yourself migration is free to license but expensive to run. Bifrost automates the work and keeps every step reversible.

CriteriaStay on ClouderaDIY open-source migrationBifrost
Licensing cost$500K-2M per year, ongoingEliminatedEliminated
Time to valueNo effort, but no improvementMonths of in-house effortWeekend cutover, phased after
Planned downtimeNoneUnpredictable, manual6-8h window, or stay online
Rollback safetyNot applicableBuild it yourselfGated, reversible until finalize
Code and pipeline changesNoneLikely rewritesNone in Classic, progressive after
ValidationVendor toolingManual and ad hocRow-count and data-diff parity
Vendor lock-inHigh, proprietaryNoneNone, open formats
Overall riskRising cost and end-of-life riskHigh execution riskLow, automated and gated
Migrations in production

Representative results across industries

Composite examples based on real-world enterprise migrations. Outcomes vary with environment, data volume, and readiness.

Financial services
65% lower cost

500TB+ trading data migrated in 12 weeks

A major financial institution migrated 500TB+ trading data from Cloudera to Kubernetes. It achieved a 65% cost reduction, 4x faster processing, and improved regulatory compliance with automated data governance, completing migration in 12 weeks with zero business disruption.

Healthcare
70% faster processing

Patient data across 200+ hospitals

A healthcare system migrated patient data across 200+ hospitals from Hadoop to a cloud-native platform. It maintained HIPAA compliance, reduced processing time by 70%, and enabled real-time analytics for patient care optimization.

E-commerce
10x performance

100M+ daily transactions at scale

A retail platform migrated its recommendation engine from Hadoop to Kubernetes, handling 100M+ daily transactions. It achieved up to 10x better performance, an 80% cost reduction, and real-time personalization at massive scale.

What an engagement looks like

A phased program, not a cliff edge

A full Modernize or Direct program runs as waves over a few months, so production keeps serving while the platform moves underneath.

Month 1

Discover and land

Estate discovery, planning, dependency analysis, and target platform landing.

Month 1-2

Bridge and quick wins

Dual-read bridge configured, first waves of quick-win tables migrated.

Month 2-4

Wave-based migration

Table-by-table migration, workload conversion, and HUE import.

Month 4-5

Decommission

Silence period, legacy service decommission, and Cloudera Manager shutdown.

Month 2-4HBase track: Runs in parallel with table migration.

A Classic license-exit cutover is a single weekend per cluster. Full Modernize or Direct programs typically run 2 to 5 months and stay phased so production keeps running throughout.

Find your path

Which path fits your estate?

Bifrost runs three paths, independently or chained. Match your situation to a starting point.

You need to cut licensing fast and stay on bare metalClassic
You are already on open-source Hadoop and want a Kubernetes lakehouseModernize
You are on Cloudera CDP and want to land on Kubernetes in one engagementDirect
You run a large multi-cluster estateClassic, then Modernize
You are regulated and need auditable, reversible stepsAny path, gated validation
Technical reference

Go deeper on the migration

The full technical detail, grouped so you can open only what you need.

Legacy estates are stranded assets

Legacy Hadoop infrastructure represents significant stranded assets as the majority of enterprises plan cloud-native migration. Organizations typically save 60-75% on infrastructure costs with Kubernetes modernization.

Cloud-native deploys far faster

Kubernetes-based data platforms typically deploy up to 90% faster than traditional Hadoop clusters. Modern containerized workloads achieve 3-5x better resource utilization with auto-scaling.

Cutover without the big bang

In-place license exits complete inside a planned weekend window, while modernization to Kubernetes migrates table by table with near-zero downtime. For large clusters, HDFS can stay online using the shrink-and-grow strategy.

Data migration at scale

Transfer petabytes efficiently with parallel cluster connectivity. Automated validation ensures 99.9%+ data integrity during migration with real-time consistency checks.

Application modernization

Move Hadoop applications to cloud-native architectures. Automated workload analysis identifies optimization opportunities and migration pathways for legacy applications.

Performance optimization

Achieve typical 3-5x performance improvements with Kubernetes resource optimization. Dynamic scaling and intelligent workload placement maximize infrastructure efficiency.

Up to 90% cost reduction vs HDFS

Object storage eliminates expensive HDFS infrastructure with pay-per-use pricing. No hardware costs, no replication overhead, no NameNode bottlenecks. S3 Standard costs about $0.023/GB/month versus an HDFS total cost of ownership of $0.20-0.50/GB including hardware, power, operations, and 3x replication.

Infinite scale without limits

Scale storage from terabytes to exabytes without cluster management or capacity planning. Object storage auto-scales instantly with no performance degradation and no DataNode capacity management or cluster expansions.

11 nines durability

Cloud object storage provides 99.999999999% durability versus HDFS 3x replication. Automatic redundancy across availability zones eliminates data loss risk, with no manual replication management or disaster recovery complexity.

Operational simplicity

Zero infrastructure management: no NameNode, DataNode, or cluster management. Automatic scaling: storage scales instantly without capacity planning. No maintenance windows: the cloud provider handles hardware, upgrades, and patches. Reduced complexity: eliminate HDFS configuration, tuning, and troubleshooting overhead.

Superior economics

Pay-per-use: only pay for storage actually used. Tiered storage: lifecycle policies move cold data to cheaper tiers (Glacier, Archive). No replication tax: built-in durability removes the 3x replication overhead. Zero capital expenditure: no hardware purchases or data-center costs.

Cloud-native integration

Native service integration: works with EMR, Databricks, Athena, Redshift Spectrum, and Azure Synapse. API-first: RESTful APIs integrate with any tool. Event-driven: trigger Lambda or Cloud Functions on object events. Multi-cloud: S3-compatible APIs work across AWS, Azure, GCP, and on-premises.

Performance and security

Columnar formats: Parquet and ORC on object storage can deliver 5-50x faster queries depending on data layout. Fine-grained access control: IAM, bucket policies, and ACLs. Encryption: server-side and client-side with key management. Open table formats: Delta Lake, Iceberg, and Hudi for ACID and time travel.

Assessment and planning

Data inventory: analyze HDFS volume, access patterns, and retention. Cost modeling: calculate object-storage costs including tiers and transfer. Access pattern analysis: identify hot, warm, and cold data for tier placement. Timeline planning: develop a phased approach based on workload priority.

Parallel migration execution

Incremental transfer: migrate data in phases while HDFS stays available. Validation framework: automated checksums, row counts, and integrity verification. Performance: parallel transfer agents maximize network use. Low downtime: applications keep running on HDFS during migration with a transparent cutover.

Cutover and validation

Application migration: update apps to object-storage APIs (S3A, ABFS). Performance testing: validate query performance and optimize. Decommissioning: retire HDFS after a validation period. Cost validation: monitor object-storage costs and optimize tiers.

Cloudera Manager migration

Components: cluster management, service configuration, user management, security policies. Challenge: centralized Cloudera Manager control is replaced with Kubernetes-native orchestration and GitOps. Solution: automated migration of configurations to Kubernetes manifests with Helm charts and operators.

Cloudera Data Platform modernization

Components: data engineering, data warehouse, machine learning, operational database. Challenge: CDP workloads need cloud-native alternatives while preserving processing capability. Solution: containerized equivalents using Spark on Kubernetes, cloud-native ML platforms, and modern data warehouses.

Enterprise security framework

Components: Kerberos authentication, Ranger policies, Knox gateway, data encryption. Challenge: the Cloudera security model is translated to Kubernetes RBAC and cloud-native frameworks. Solution: integration with cloud IAM, Kubernetes security policies, and enterprise identity providers with policy automation.

Cloudera Data Engineering to Spark on Kubernetes

Migrate CDE workloads to cloud-native Spark with Kubernetes operators. Benefits: auto-scaling, cost optimization, multi-cloud portability. Migration time: 4-8 weeks for typical workloads. Improvement: 3-5x better resource utilization with dynamic allocation.

Cloudera Data Warehouse to modern analytics

Transform CDW to cloud-native analytics on Kubernetes. Alternatives: Trino, Spark SQL, cloud data warehouses. Cost savings: 70% reduction in query processing costs. Scalability: elastic scaling based on query demand.

Cloudera Machine Learning to MLOps platforms

Modernize CML workloads with Kubernetes-native ML platforms. Technologies: Kubeflow, MLflow, cloud ML services. Advantages: better model deployment, automated pipelines, improved collaboration. Time to value: 50% faster model deployment cycles.

Cloudera Manager dependencies

Commercial features: automated cluster management, service configuration, monitoring dashboards, backup and recovery. Migration strategy: replace with Kubernetes operators, Helm charts, and cloud-native monitoring. Benefits: GitOps workflows, infrastructure as code, cloud-native automation.

Enterprise security differences

Cloudera security: integrated Kerberos, Ranger policies, Knox gateway, Sentry permissions. Open-source Hadoop: basic security with manual configuration. Kubernetes: RBAC, service-mesh security, cloud IAM integration, policy automation.

Licensing and support models

Cloudera: per-node licensing, commercial support contracts, vendor lock-in. Open source: community support, no licensing costs. Cloud-native: pay-as-you-use, vendor independence, community and commercial support options.

Encryption and security standards

Data protection: encryption in transit and at rest to meet enterprise security requirements. Protocols: AES-256 for stored data, TLS 1.3 in transit. Compliance: maintain GDPR, HIPAA, SOC 2, and other obligations throughout migration.

Validation and verification

Checksum validation: automated verification ensures byte-level accuracy during transfer. Reconciliation: row counts, schema validation, and data sampling identify discrepancies. Testing: thorough testing is key to a trustworthy migration.

Comprehensive data profiling

Pre-migration analysis: profile datasets to understand quality and characteristics before moving data. Quality metrics: identify patterns, distributions, and potential issues. Baseline: create baseline metrics for post-migration comparison.

Quality metrics and monitoring

Thresholds: set clear metrics for acceptable data quality. Continuous monitoring: validation is an integral part of the process. Alerting: automated alerts for quality violations and validation failures.

Schema transformation tools

AWS Glue: automated schema discovery and ETL transformations. Azure Data Factory: enterprise data integration with schema mapping. Apache Spark: flexible schema evolution during migration.

Lakehouse architecture

Unified platform: a lakehouse supports structured and unstructured data. Schema flexibility: handle evolution without disrupting existing workloads. Modernization: schema management helps modernize your Hadoop infrastructure effectively.

Data catalog management

Schema tracking: a robust catalog manages and tracks schema evolution. Consistent access: keep data access consistent and accurate during migration. Metadata: a centralized schema registry for enterprise-wide governance.

Resource and query optimization

Compute tuning: right-size EMR, Databricks, or Kubernetes clusters for each workload. Dynamic scaling: adjust resources to demand and use spot or preemptible instances where safe. Query performance: partition tables, use Parquet, ORC, or Delta Lake, and apply intelligent caching.

Monitoring and operations

Key metrics: track compute utilization, storage cost, and query response time. Observability: end-to-end visibility into pipelines and workload performance. Operational procedures: RBAC, audit logging, compliance monitoring, and incident response.

Continuous modernization

Technology evaluation: regularly assess new cloud-native services. Lakehouse architecture: support diverse data types and both batch and streaming. Serverless: adopt serverless compute to optimize cost and scalability.

Data-driven migration planning

Comprehensive workload analysis identifies optimization opportunities and migration priorities. Automated assessment tools analyze data patterns, access frequencies, and performance requirements to optimize migration strategy and resource allocation.

Incremental migration strategy

A phase-based approach minimizes risk and enables continuous validation. Start with non-critical workloads, validate performance and data integrity, then progressively migrate mission-critical applications.

Risk mitigation and rollback

Comprehensive contingency planning with automated rollback. Real-time monitoring detects issues early, while parallel systems enable instant rollback if performance or data integrity problems arise.

Infrastructure and benchmarks

Recommended specs: 16 vCPUs, 64GB RAM per migration node, 10Gbps networking for typical enterprise migrations. Transfer rates: up to 10GB/s with parallel connectivity at a 99.9% success rate. Migration speed: 50-100TB per day depending on network and storage. Actual requirements vary with data volume and complexity.

Security and compliance

Encryption: AES-256 in transit and at rest, TLS 1.3 for all communications. Access controls: role-based authentication, audit logging, and SOC 2, GDPR, and HIPAA alignment. Governance: automated lineage tracking and policy enforcement.

Toolkit and ecosystem integration

Ecosystem: works with Apache NiFi, Kafka, Spark, and Airflow, with native support for Cloudera Manager and Ambari. Multi-cloud: deploy across AWS, Azure, GCP, and hybrid with a consistent experience. Future-ready: Kubernetes-native design for serverless, edge, and AI/ML workloads.
FAQ

Hadoop and Cloudera migration, answered

The questions enterprise teams ask most about exiting Cloudera with Bifrost.

Bifrost is Ilum's purpose-built Hadoop and Cloudera CDP migration toolkit. It is a command-line migration framework that automates discovery, configuration translation, data movement, validation, and rollback, taking enterprises through the full lifecycle of Hadoop modernization with auditable, reproducible, go/no-go gated steps.

Yes. For an in-place license exit (Cloudera CDP to open-source Hadoop), Bifrost performs a stop-and-swap cutover within a planned weekend window, typically around 6-8 hours of overnight downtime to swap packages and upgrade HDFS, followed by automated validation and benchmarking before Monday. Larger 100+ node clusters can instead use the shrink-and-grow strategy, which keeps HDFS online throughout and migrates nodes in batches over a few days.

Yes. During an in-place migration, data directories on source nodes are never touched. Every step is reversible until an explicit, final step: a multi-day soak period keeps a full rollback option open (roughly a 4-hour revert), and row-count plus value-level data-diff checks must pass before any migrated table is treated as authoritative. Any detected mismatch aborts the migration.

Bifrost maps the full Hadoop and Cloudera stack to modern equivalents: HDFS to S3A-compatible object storage, YARN to Spark on Kubernetes, the Hive Metastore to an Apache Iceberg REST catalog, Hive and Impala to Trino, Oozie to Apache Airflow, Ranger to Open Policy Agent, HBase to Cassandra or ScyllaDB, and Atlas to OpenMetadata. It is explicit about automation ceilings, what it fully automates versus what needs manual follow-up.

No. The Classic path replatforms Cloudera CDP onto open-source Apache Hadoop in place, eliminating licensing while preserving your existing architecture and bare-metal footprint. You can modernize to Ilum on Kubernetes later with the Modernize path, or do both at once with the Direct path.

No, not for the Classic license exit. Bifrost preserves the cluster architecture and creates compatibility symlinks, so Spark and Hive jobs, Oozie workflows, SQL, and BI dashboards keep running unchanged on open-source Hadoop. When you later modernize to Kubernetes, a dual-read bridge and Trino table redirection keep consumers working while storage and orchestration move underneath, so modernization such as Oozie to Airflow happens progressively rather than as a forced big-bang rewrite.

Organizations typically save $500K-2M annually by eliminating Cloudera licensing fees, depending on deployment size. Combined with reduced support costs and avoided hardware refresh cycles, total infrastructure cost savings commonly range from 60-75% of current Cloudera infrastructure expenses.

It depends on the path. A Classic in-place license exit cuts over in a single weekend per cluster, with a 4 to 6 month program for a large multi-cluster estate. Modernize and Direct programs, which move you to a Kubernetes lakehouse, typically run 2 to 5 months and are fully phased, so production keeps running throughout.

Bifrost fits Cloudera CDP 7.x and open-source Hadoop estates that mix HDFS, YARN, Hive, Oozie, and Ranger. If you need to exit Cloudera licensing fast while staying on bare metal, start with Classic. If you are ready for Kubernetes, use Modernize or Direct. A migration assessment confirms the right path and surfaces the automation ceiling for your specific workloads.

No. The destination uses open formats and open-source components: Apache Iceberg tables, Parquet and ORC files, S3-compatible APIs, and open catalogs. The exit path is not obstructed by proprietary data formats or catalog implementations, so you keep the option to leave and run the open-source components directly.

Start with a migration assessment. Bifrost runs an automated discovery of your estate and produces a scored migration plan with wave assignments, a total cost of ownership projection, and an automation-ceiling report, along with a recommended path. Contact [email protected] or request an assessment to begin.

Start here

Book a migration assessment

See exactly what leaving Cloudera looks like for your estate before you commit a single change.

Bifrost is a commercial component of Ilum Enterprise.

Your assessment delivers

  • Automated estate discovery and inventory
  • A scored migration plan with wave assignments
  • A total cost of ownership projection
  • An automation-ceiling report: what is automated versus manual
  • A recommended migration path

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Disclaimer

Performance improvements, cost savings, and migration timelines on this page represent typical outcomes based on enterprise experience and industry benchmarks. Actual results vary with your environment, data volume, workload characteristics, infrastructure, and organizational readiness. Case studies are composite examples based on real-world scenarios. Cloud pricing is accurate as of publication but subject to change. We recommend a thorough assessment and proof-of-concept for your specific use case.

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