MD

Mark D.

Head of Technology

Banking

Enterprise (\> 1000 emp.)

10/26/2025

"From Hadoop to K8s with lower TCO"

5/5

What do you like best about ILUM?

We can own the entire environment on-prem today and move it to our private cloud tomorrow without changing the way teams work, the Kubernetes-first model makes that portability real. For the organization, that means modernization on our terms: virtual clusters with quotas per team, job-level cost guidance, built-in lineage/ERD, and an OSS toolchain (Airflow, dbt, MLflow, Superset) that integrates cleanly. For the team, day-to-day delivery is faster: the SQL editor/notebooks and Jupyter are in one place, deployments are straightforward, and we use it every day across dev/UAT/prod without juggling licenses. Implementation was practical rather than painful, Hive Metastore slotted in, and integration with existing pipelines was mostly configuration, not rewrites. Review collected by and hosted on G2.com.

What do you dislike about ILUM?

Owning the stack means taking Kubernetes seriously: capacity planning, RBAC, and policy hardening need attention up front, especially if you’re moving from a managed Hadoop/YARN world. Lineage can be noisy until you define domains/tags, and centralizing logs from the integrated OSS tools into your SIEM takes a bit of plumbing. None of this is blocking, but you should plan a short enablement sprint so the platform lands well for both the team and the business. Review collected by and hosted on G2.com.

What problems is ILUM solving and how is that benefiting you?

Ilum solves three concrete problems for us: getting off aging Hadoop, taking control of costs, and speeding up delivery. We moved our estate from Cloudera/YARN to a Kubernetes lakehouse we can run fully on-prem today and shift to our private cloud or a mixed K8s setup without rewriting pipelines. Job-level Spark cost insights and configuration recommendations stopped over-provisioning, while virtual clusters with quotas let us allocate budget by team and keep runaway spend in check. The integrated OSS stack (Airflow, dbt, MLflow, Superset) replaced multiple paid tools, and automatic lineage/ERD/column lineage gives audit-ready traceability for regulatory change. For the team, the built-in SQL editor, notebooks, and Jupyter made engineering tasks faster and releases simpler across dev/UAT/prod. Net result: lower TCO, predictable operations we own, and a modern platform that actually accelerates delivery. Review collected by and hosted on G2.com.

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