Hadoop Consulting Services
End-to-end Hadoop ecosystem modernization—from stable foundations to production analytics and AI.
25+
Years of data expertise
100K+
Workloads migrated or managed
45+
Technology specializations
Hadoop use cases: Turning ecosystem complexity into competitive advantage
Production-ready Hadoop solutions for every stage of your journey.
Stabilize and secure
High-performance Hadoop operations
We identify bottlenecks, tune performance, and reduce risk across your cluster. For unsupported distributions, we build remediation plans to keep mission-critical workloads safe.
Map every dependency
Workload inventory and dependency mapping
We catalog every workload, pipeline, and policy, then map the dependencies between them—the step where most migrations fail without deep Hadoop expertise.
Upgrade within Cloudera
Cloudera CDP modernization
We upgrade your environment to CDP, preserving existing workloads and security models while unlocking modern capabilities like Iceberg and hybrid deployment.
Exit to cloud-native
Cloud-native platform migration
We deliver low-risk migrations to Databricks, BigQuery, Snowflake, and other cloud platforms—converting code, workflows, and data at petabyte scale.
Modernize pipelines and orchestration
Data engineering and automation
We replace fragmented ETL with modern, observable pipelines and remap security to cloud-native frameworks—turning migration into a chance to eliminate technical debt.
Unlock analytics and AI
Production analytics and AI enablement
We turn batch reports into real-time dashboards and integrate ML workflows into your new platform. Data trapped in HDFS becomes fuel for production AI.
Explore Pythian's data migration consulting services
Modernize your entire data ecosystem to gain a competitive advantage.
Whether you're migrating Hadoop to the cloud or modernizing your entire data platform, Pythian's data migration consultants deliver seamless, low-risk transitions at any scale—with decades of experience across 45+ technologies.
Hadoop modernization frameworks that ensure zero-disruption during migration
A phased, ecosystem-aware approach that keeps your business running while we transform every layer of the stack.
We assess cluster health, performance, and security posture. For unsupported distributions, we stabilize mission-critical workloads while planning the path forward.
We catalog every workload, pipeline, and policy, then map the dependencies—the critical prerequisite most programs miss.
We recommend the right path—CDP upgrade, cloud-native exit, or hybrid—based on workload analysis and ROI modeling, with phased milestones for quick wins.
We migrate data, code, workflows, security, and metadata to their targets. Dual-run validation ensures zero disruption.
We deliver modern dashboards, self-service analytics, and AI-ready pipelines—plus 24/7 support so your team can focus on extracting value.
Ready to transform your Hadoop environment?
Pythian's related Hadoop services
Our end-to-end data services ensure your Hadoop modernization delivers lasting business value.
Build reliable, governed data pipelines
Data engineering consulting
We have deep expertise in legacy and cloud-native databases. We keep mission-critical systems running at peak reliability.
Move your infrastructure to the cloud
Cloud migration consulting
Every Hadoop component mapped to its optimal target. We handle the full complexity of multi-component modernization.
Align your data investments with business goals
Data strategy and management consulting
From batch reports to real-time insights. Business users get answers in seconds, not the hours Hadoop required.
Hadoop consulting services frequently asked questions (FAQ)
Security is built into every phase of our migration process. We start with a comprehensive security vulnerability assessment of your existing cluster—critical for organizations running unsupported CDH or HDP distributions. During migration, we remap your Kerberos authentication and Apache Ranger fine-grained access controls to cloud-native IAM frameworks (AWS IAM, GCP IAM, Azure AD), preserving the access policies that regulated industries depend on. We also migrate your data governance metadata from Apache Atlas to modern platforms like Google Dataplex, Collibra, or Unity Catalog. Dual-run validation ensures no gaps in security coverage during the transition.
The ROI from Hadoop modernization comes from multiple sources. Infrastructure cost savings are often the most immediate win—one customer eliminated nearly $500K per year in on-premises hardware and Cloudera licensing costs. Beyond cost reduction, organizations typically see 10x or greater query performance improvements over legacy Hive on MapReduce, dramatic reductions in operational complexity (from managing 20+ ecosystem components to a single managed platform), and the ability to enable self-service analytics and production AI capabilities that were impossible on the legacy cluster. The phased approach we recommend means you start seeing returns on high-value workloads early in the migration—not just at the end.
Automated conversion tools can typically handle 70–90 percent of standard HiveQL queries. However, the remaining queries—those with complex nested data types, custom SerDes, and Java UDFs—require manual refactoring by engineers who understand both the source and target platforms. MapReduce jobs and Pig scripts have no direct automated conversion path; they must be rewritten as Spark jobs, PySpark, or cloud-optimized SQL. Oozie workflow migration to Apache Airflow or Databricks Workflows is similarly labor-intensive because each DAG coordinates multiple interdependent Hive, Spark, MapReduce, and shell jobs. This is precisely where Pythian's deep Hadoop ecosystem expertise makes the difference—we've done this work across complex, multi-petabyte environments and know where the hidden dependencies live.
It depends on your workload characteristics, strategic direction, and budget. CDP is a strong choice for organizations that want to preserve their existing Hive tables, Spark jobs, and security models while gaining modern capabilities like Apache Iceberg support and hybrid cloud deployment. Cloud-native platforms like Databricks, BigQuery, or Snowflake are better suited for organizations ready to fully decouple from the Hadoop ecosystem and gain serverless scaling, modern BI integration, and AI-ready infrastructure. A third option—phased hybrid—lets you migrate high-value workloads to cloud-native platforms first while maintaining the Hadoop cluster for lower-priority batch jobs during the transition. Pythian provides vendor-neutral assessment based on workload analysis and ROI modeling, not vendor loyalty.
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