This joint webinar with Dremio presents a practical, phased path off Hadoop for organizations running HDFS at scale. The session opens with an honest accounting of Hadoop's architectural limitations, including coupled storage and compute, absent ACID compliance, poor SQL concurrency, and high operational complexity, before introducing a three-stage modernization approach. Stage one deploys Dremio as a query acceleration layer in front of existing HDFS without touching data. Stage two migrates HDFS to MinIO AIStor using MC mirror or distcp, preserving namespace and prefix structure. Stage three converts Hive tables to Apache Iceberg for ACID compliance, schema evolution, and open query engine interoperability. A Nomura financial services case study illustrates what the fully migrated end state looks like in a production hybrid environment.
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The three-stage migration approach, Dremio as query layer first, HDFS-to-MinIO data migration second, Hive-to-Iceberg conversion third, lets organizations improve analytics performance immediately without disrupting existing data or applications, with each stage delivering independent value.
MinIO AIStor maintains the full namespace and prefix structure of HDFS during migration, meaning Spark and other applications can switch from HDFS URLs to S3A connector access with no changes to data organization or application logic.
Nomura achieved over 50% query performance improvement and simplified hybrid infrastructure by running MinIO on-premises alongside S3 in the cloud, with Dremio clusters on both EKS and on-premises Kubernetes federating both environments, while retaining all existing Spark pipelines and Jupyter notebook workflows unchanged.
Data engineering leads, infrastructure architects, and analytics platform owners at organizations running Hadoop or HDFS who are evaluating a migration path to a modern, decoupled lakehouse without a disruptive rip-and-replace.
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