Automated on-premise Hadoop (CDH) migration to a modern data lake & ETL stack on GCP

Challenge

The customer ran a multi-petabyte on-prem Cloudera CDH cluster with thousands of Hive tables, hundreds of Oozie and Airflow jobs, and dependent BI & data-science workloads. Cloudera’s end-of-support timeline, mounting hardware costs, and the impossibility of elastic scaling made a cloud move non-negotiable.

The risk: a manual lift-and-shift would consume years and break critical pipelines. The team needed an automated migration with provable parity and minimal disruption.

Approach

VerticalServe’s POD designed and executed an automation-first migration:

• Discovery automation that crawled the source cluster — Hive metastore, HDFS layout, job schedulers, queries — to produce an inventory and dependency graph
• Schema & data movers for HDFS → GCS and Hive → BigQuery with type-mapping rules and validation
• Job translators that ported Hive SQL, Spark, and Oozie workflows to BigQuery SQL, Dataproc Serverless Spark, and Cloud Composer (Airflow)
• Automated validation comparing row counts, distributions, and golden-query results between legacy and target
• Cutover orchestration in waves by domain, with rollback paths and dual-run for critical pipelines
• FinOps controls on BigQuery slots and Dataproc autoscaling from day one

Outcome

The customer decommissioned the on-prem CDH footprint inside one budget cycle. Pipelines became elastic; BI users noticed faster queries; the data-engineering team stopped firefighting capacity issues and started shipping features. The automation tooling was open-sourced internally for use on subsequent platform programs.

Impact

Stack

GCP BigQuery GCS Dataproc Cloud Composer (Airflow) Cloud Functions Cloudera CDH (source) Hive Spark Terraform

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