Hadoop to Kubernetes Migration Playbook: What Platform Teams Should Know First

Migrating from Hadoop to Kubernetes represents an architectural transformation rather than a simple operational shift, requiring deliberate replacements for components like YARN scheduling and HDFS storage. Teams that approach this migration with phased, parallel work streams tend to achieve better outcomes compared to those opting for a big-bang approach. Key to a successful transition are four foundational decisions: choosing the right storage destination, compute scheduler, workload engine, and governance model, all of which are interdependent and can lead to significant rework if mismanaged. The process involves moving data from HDFS to S3-compatible storage, rewriting Hive jobs for Spark SQL, and replacing YARN with Kubernetes-native schedulers such as Apache YuniKorn to handle data workload characteristics effectively. Cloudera migrations add complexity due to proprietary dependencies, requiring replacements with open-source solutions like Apache Gravitino and Ranger. The migration strategy benefits from running both Hadoop and Kubernetes environments in parallel to minimize risks, with careful sequencing of irreversible and reversible decisions. Acceldata xLake facilitates this phased migration by maintaining HDFS compatibility, allowing teams to progressively validate and migrate workloads without the risks associated with a big-bang cutover.