Hadoop Consulting Services
End-to-end Hadoop ecosystem modernization—from stable foundations to production analytics.
25+
Years of data expertise
100K+
Workloads migrated or managed
45+
Technology specializations
Hadoop services that ensure zero-disruption migrations
Stabilize
Hadoop ecosystem health assessment
We assess your full Hadoop environment and catalog every workload, pipeline, and dependency. You get a benchmarked baseline with a prioritized remediation plan for unsupported CDH or HDP clusters.
Optimize
Performance tuning and governance hardening
We optimize HDFS storage, tune YARN resource allocation, and more—making your current investment work harder while establishing the governance baseline your migration depends on.
Migrate and modernize
Platform migration and pipeline modernization
Pythian migrates Hadoop workloads to Databricks, BigQuery, and Snowflake. Your modernized platform unlocks analytics and capabilities that weren't feasible on Hadoop.
How we work with you
Plan your data modernization pathway
We assess cluster health, performance, and security posture. For unsupported distributions, we stabilize mission-critical workloads while planning the path forward.
Catalog your data workloads
We catalog every workload, pipeline, and policy, then map the dependencies—the critical prerequisite most programs miss.
Strategize your data roadmap
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.
Data migration without disruption
We migrate data, code, workflows, security, and metadata to their targets. Dual-run validation ensures zero disruption.
Continued data ecosystem support
We deliver modern dashboards, self-service analytics, and AI-ready pipelines—plus 24/7 support so your team can focus on extracting value.
Modernizing big data infrastructure from Hadoop for a tier-1 financial institution
From unsupported Hadoop to governed Databricks Lakehouse—55% cost reduction and production AI in under a year.
A global financial institution with $200B+ in assets under management ran regulatory reporting, fraud detection, and risk analytics on an aging, unsupported Hadoop cluster. Pythian inventoried the 25+ component ecosystem, executed a phased migration, and delivered $1.3M in annual savings, 10x query performance, and a production-ready AI foundation.
Pythian: The enterprise migration leader
Modern platform migrations from Hadoop
Databricks
Transition Hadoop data lakes and Spark workloads to the Databricks Lakehouse platform for unified governance, AI capabilities, and simplified infrastructure management.
Snowflake
Migrate HDFS data and MapReduce pipelines to Snowflake’s Data Cloud to enable scalable analytics with minimal infrastructure management.
Redshift
Re-platform Hadoop environments to AWS using Amazon EMR, Amazon S3 data lakes, or Redshift for scalable cloud-native analytics.
BigQuery
Modernize Hadoop platforms by migrating to Google Cloud services such as BigQuery and Dataproc to support serverless analytics and Spark workloads.
Microsoft Fabric
Move legacy Hadoop clusters to Azure using Azure Databricks, Azure Data Lake Storage, or Synapse Analytics.
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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