The inventory and the timeline you need before committing to a Hadoop exit

Overview

Before committing budget to a Hadoop migration, leadership needs an evidence-based answer to what is on the cluster, what still matters, and how long the exit will take. AceMQ produces that inventory and a sequenced plan with effort estimates.

Challenge

The cluster's real state is undocumented. Nobody can say which of the several thousand Hive tables are still read, which YARN queues carry production work versus abandoned experiments, or which jobs feed regulatory reporting and therefore cannot be interrupted. Estimates offered without this information are guesses, and migration programs built on guesses run long.

Environment

On-premises Hadoop clusters with HDFS, Hive, YARN, and mixed Spark, MapReduce, and Hive workloads under a commercial or community distribution.

Approach

AceMQ instruments the cluster's own audit and query logs over a representative period rather than relying on interviews. Every table gets a last-read timestamp and consumer list; every job gets a runtime, resource, and criticality profile. Operating cost is modeled including hardware, support, power, and the staff time absorbed by cluster maintenance, and compared against the target platform.

Solution

• Table-level access profiling from HDFS audit and HiveServer2 logs, giving each dataset a last-read date and consumer list
• Job inventory across YARN queues with runtime, resource footprint, and business criticality classification
• Current-state cost model covering hardware refresh, distribution support, power and rack, and operational staff time
• Version and CVE exposure review across the distribution's component stack
• Migration wave sequencing ordered by dependency depth and business risk, with effort estimates per wave
• Retire-versus-migrate recommendation for every dataset and job, with dormant data flagged for archival rather than migration

Outcome

Technologies