Ilum

Ilum is a Spark-powered data lakehouse that unifies storage, processing, and analytics for modern data teams. Deployed in the cloud, on-premises, or in hybrid topologies, it supports open table formats (Delta, Iceberg, and Hudi), so existing data can be queried without lock-in. It is used to simplify data management and enable AI innovation.

Key Capabilities

• Modular Tooling – One-click enablement of Jupyter Notebooks, Apache Superset, MLflow, and dbt streamlines engineering, analytics, and MLOps in a single workspace.

• Multi-Cluster Management – Run multiple Spark clusters side by side to benchmark engines, isolate workloads, or elastically scale from laptop to petabyte deployments.

• Governed SQL & BI Access – A built-in SQL editor plus certified connectors for Tableau and Power BI give analysts governed, real-time access to lakehouse data.

• Automatic Data Lineage – End-to-end lineage graphs and column-level impact analysis simplify audit, debugging, and regulatory compliance.
  
  

Deployment & ROI

Ilum can replace aging Hadoop stacks such as Cloudera or serve as a cost-neutral alternative to commercial lakehouse platforms. Users report cluster provisioning in minutes instead of hours and material license savings because the core platform is free to use.

Differentiators

Ilum combines no-cost licensing, open-format storage, and plug-and-play data tools in one package. Its ability to run identically in local, on-prem, or cloud environments allows organizations to modernize analytics and AI workflows at their own pace, without proprietary constraints.