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Ilum

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4.9
Serving customers since
2019
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ILUM

23 reviews

Ilum is a free data lakehouse platform designed for scalability, flexibility, and simplicity.

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Ilum

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Ilum is a comprehensive data lakehouse platform designed to streamline the management and monitoring of Apache Spark clusters across cloud, on-premise, and hybrid environments. It integrates seamlessly with tools like Jupyter, Apache Airflow, and MLflow, providing a unified solution for data scientists, cloud engineers, data analysts, IT administrators, and machine learning engineers. Ilum supports open table formats such as Delta Lake, Apache Iceberg, and Apache Hudi, ensuring flexibility and avoiding vendor lock-in. Its Kubernetes-native architecture offers scalability, high availability, and dynamic resource management, making it a modern alternative to traditional data platforms. Key Features and Functionality: - Unified Multi-Cluster Management: Manage multiple Spark clusters across various environments through a single platform. - Interactive Spark Sessions: Engage with Spark jobs via a REST API and user-friendly web interface, eliminating the need for command-line interactions. - Integration with Data Tools: Seamlessly integrates with Jupyter, Apache Airflow, MLflow, and business intelligence tools like Tableau and Power BI. - Support for Open Table Formats: Works with Delta Lake, Apache Iceberg, and Apache Hudi, ensuring ACID compliance and efficient data storage. - Kubernetes and Hadoop Yarn Integration: Facilitates easy deployment and management of Spark jobs on Kubernetes and integrates with Apache Hadoop Yarn. - Scalability and High Availability: Offers horizontal scalability and dynamic resource scaling to handle workloads of any size. - Data Governance and Security: Provides data lineage tracking, role-based access control, and integration with Apache Ranger for enhanced security. Primary Value and Problem Solved: Ilum addresses the challenges of managing and monitoring Apache Spark clusters by providing a unified, scalable, and flexible platform. It simplifies operations across diverse environments, supports open table formats to prevent vendor lock-in, and integrates with a wide range of data tools. By offering interactive sessions, multi-cluster management, and robust data governance, Ilum enhances operational efficiency, accelerates data processing tasks, and empowers organizations to build and deploy data-driven applications with ease.

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Verified User in Consulting
UC
Verified User in Consulting
08/13/2025
Validated Reviewer
Verified Current User
Review source: Organic

Practical Spark on Kubernetes with clear lineage and fewer surprises

|ILUM
We run Spark on Kubernetes and ILUM gave us one place to submit jobs (via Livy), watch runs, and debug without hopping between tools. The lineage view is genuinely useful—when a column rename or schema drift sneaks in, we can trace where it broke instead of guessing. Helm install was straightforward, MinIO/S3 integration just worked, and the Airflow/Jupyter handoff feels natural. It’s not flashy, but it removed a lot of DIY glue and made day-to-day ETL more predictable. We use it every day—most mornings start on the runs page to check pipelines, and ad-hoc jobs go through ILUM as a habit now.
Mateusz K.
MK
Mateusz K.
Java Developer
08/09/2025
Validated Reviewer
Review source: Organic

ILUM: A Lakehouse Platform That Truly Surprises

|ILUM
What I like best about ILUM is how quickly and easily I can get Spark jobs running, no matter if I’m in the cloud or on-premise. The setup is incredibly smooth, and the platform scales effortlessly as my needs grow. I love the interactive session management—it saves me a ton of time on analytics and experimentation. The web UI is intuitive and makes controlling everything a breeze. Honestly, even after years of use, ILUM still surprises me with how reliable and flexible it is.
BD
Bradley D.
08/08/2025
Validated Reviewer
Verified Current User
Review source: Organic

Reliable Data Infrastructure for Real-World Engineering

|ILUM
What we like most about Ilum is that it works the way we need it to. No extra layers of complexity. No surprises. We deal with a mix of data from hardware tests, system logs, simulation outputs, and engineering tools. Before Ilum, pulling that all together was slow and error-prone. Ilum gave us a clean foundation from the start. We did not need a huge rollout or long planning sessions. We got it up and running fast, using our existing systems. That was a major win for our team. It connected smoothly with the tools we were already using. Data Catalog, SQL, BI Tools, Spark, Jupyter, MLflow, and Airflow all worked out of the box. We did not have to rebuild or rewire anything. It saved us time and helped us avoid months of migration effort. The interface is simple and makes sense. Engineers can jump in, run queries, explore datasets, and manage jobs without needing to learn a new language. It became part of our workflow right away and continues to be something we rely on every day. Support has been reliable and responsive. When we reach out, we get helpful answers quickly from people who understand real use cases, not just the surface of the product. There is also a lot of depth. Ilum handles versioned datasets, job scheduling, lineage tracking, and query access all in one environment. That kind of power without extra weight is rare. What we value most is that Ilum does not try to take over our workflow. It fits into it and makes it better. For us, that is what made it the right choice.

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Santa Fe, US

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What is Ilum?

Ilum - Free Data Lakehouse

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Year Founded
2019
Website
ilum.cloud