ILUM Reviews & Product Details

What I appreciate most about ILUM is its Ease of Integration. The platform is built to be open and modular, allowing it to connect seamlessly with the tools we already rely on, such as Airflow, dbt, Jupyter, and various BI tools through JDBC. We didn't have to overhaul our existing workflows; ILUM integrated effortlessly, which meant less hassle during implementation and a much quicker realization of value—something I consider a major advantage.

Another standout aspect is the impressive range of features combined with how easy it is to use. Since ILUM is a unified platform, I can run SQL queries, review data lineage, and launch Spark jobs or notebooks all from a single interface, eliminating the need to constantly switch between different tools. Having that centralized control is incredibly convenient.

The process of implementing ILUM is both straightforward and nuanced, but for the most part, it's simple if you have the right infrastructure in place. Because ILUM is Kubernetes-native and deployed via Helm charts, if you already have a Kubernetes cluster set up, you can get the core components—Spark and the UI—up and running in less than five minutes using a basic Helm command.

Personally, I use ILUM as my main platform nearly every day in my work. Review collected by and hosted on G2.com.

If I had to nitpick, the initial UI for the truly deep engineering stuff can be a little much for new users, but once you get past the initial setup (which is covered well by Customer Support and training), the daily use is very intuitive. The Interactive Sessions feature is also amazing for cutting down on Spark job startup time, making my day-to-day work way more efficient. Review collected by and hosted on G2.com.

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.

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.

Being able to manage and monitor my Spark cluster in an on-premise environment through a single, user-friendly web interface, as well as through a REST API, has greatly simplified the process of launching Spark jobs for my client applications, integration was not a problem thanks to well documented REST API. ILUMs has saved me considerable time and made managing Kubernetes-based Spark deployments much easier. I also appreciate its support for the Delta format, which is the one I rely on most often. Overall, ILUM has significantly streamlined the implementation of a full-stack data lakehouse solution for both myself and my company. Review collected by and hosted on G2.com.

The only aspect that might be challenging, though not exactly a drawback, is that you need some basic knowledge of K8S when starting with ILUM. Fortunately, the documentation provided by ILUM is very helpful and makes the learning process much easier. Review collected by and hosted on G2.com.

What makes ilum the best option for me is its ability to run on-premise within our company's isolated, air-gapped data center. For testing and UAT, we are able to use our private cloud, which adds flexibility. Ilum stands out because it operates seamlessly both in the cloud and on-premise. I also find it very user-friendly; our initial test deployment took just one day. We now use ilum daily. The customer support team is excellent, often responding even faster than our standard SLA requires. The range of features matches industry standards, but what impresses me most is how quickly new feature requests are implemented—sometimes they appear in the very next release. Integration with external modules is also outstanding, overall implementation is also great. Review collected by and hosted on G2.com.

I haven't encountered any major issues. However, it would be helpful to have a few additional modules focused on ETL. Review collected by and hosted on G2.com.

What I like most about ILUM is how smoothly it integrates with my existing systems and how much it simplifies working with Spark on Kubernetes. I use ILUM to connect my internal app with ILUM services, allowing me to run Spark sessions as microservices and handle heavy data computations quickly and efficiently. This setup lets me manage Spark workloads without having to worry about complex infrastructure — ILUM takes care of it behind the scenes.

I store and organize my data in Delta tables using ILUM’s built-in integration, which I find extremely useful for daily analytics. I frequently query my data through the ILUM-SQL JDBC interface and visualize results using Superset, which is tightly integrated into the platform. This combination gives me a smooth workflow from data processing to visualization, all within one environment.

Deployment was straightforward — I run ILUM in my own on-prem Kubernetes cluster, and it only required basic K8s knowledge to get everything up and running. Once deployed, it has been stable and reliable. The UI is one of my favorite parts: it’s clean, intuitive, and provides an all-in-one overview of everything from Spark jobs to data tables and SQL queries.

Lastly, the support team has been excellent — every time I had a question or issue, they responded quickly with helpful, practical answers. Overall, ILUM provides a powerful, easy-to-use environment for managing Spark workloads, organizing data, and analyzing it visually, all in one integrated platform. Review collected by and hosted on G2.com.

There isn’t much to dislike, but a few areas could still be improved. Since ILUM provides such a wide range of features in one platform, some advanced configuration options can feel a bit hidden or require digging through documentation to fully understand. While the UI is very user-friendly for daily work, some of the deeper administrative settings — like custom Spark configurations or advanced access control — could be easier to manage directly from the interface.

Because I run ILUM on-prem in my own Kubernetes environment, initial tuning of resources and storage integration required some experimentation to find the right balance. It would be great to have more examples or automation for these parts of the setup.

Also, although ILUM’s Superset integration works very well, large queries or visualizations sometimes take time to load when working with very big Delta tables — not a dealbreaker, but something to keep optimizing.

Overall, these are minor issues compared to the value ILUM provides. The platform is improving quickly, and support has been responsive whenever I’ve needed help fine-tuning configurations. Review collected by and hosted on G2.com.

It turns delivery into a repeatable practice instead of glue work. I open a PR, pick a template, and get a compliant workspace with CI, secrets, RBAC, cost tags, approvals, and lineage set the same way every time. In numbers, we run about 180 scheduled pipelines and 30 long-running services, roughly 600 to 800 runs per day, with a 99.2% success rate last quarter. MTTR fell from 85 to 38 minutes and change failure rate sits under 5%. Ease of implementation was real, not marketing. We were live in two sprints, and ease of integration matched that story: SSO and Git mapped cleanly, storage and the warehouse stayed where they were, Kafka and observability slotted in, no code rewrites, policies stayed intact. Ease of use is strong too: the UI is clear, paved templates keep teams on the rails, errors include actionable hints, and one console for jobs, lineage, and cost means less tool hopping. Our first pipeline hit prod in week one. Support is the safety net I trust. During a release freeze we hit a nasty networking edge case, an engineer joined live, traced the egress policy, and we shipped that afternoon. A later P1 around permissions closed in under 48 hours with a runbook we still use. Day to day it feels like a platform for professionals that helps us meet SLOs, not a toolbox we have to assemble. Review collected by and hosted on G2.com.

It is not a one-click black box - some Kubernetes basics help at the start, especially networking, storage classes, and RBAC. The console is more functional than flashy; a native dependency timeline with simple bulk actions like pause or owner change would save us scripting. Advanced networking and GPU scheduling needed a bit of hand holding. On the plus side, support actually shows up: they replied same day to the timeline request and put us on early access, and a P1 around egress rules was resolved in under 48 hours with a clear runbook we still use. Once the guardrails are set, it is stable and predictable, so these are minor UX and learning-curve items rather than daily blockers. Review collected by and hosted on G2.com.

What I like most about Ilum is that it runs where you need it to run. On-prem, in the cloud, hybrid, it doesn’t really matter. You're not boxed into someone else’s ecosystem or forced to rebuild everything from scratch.

That flexibility matters when you already have a working stack. We didn’t have to rip out tools we trust or jump through hoops to make Ilum fit. It plugged into what we had, Kubernetes, Spark, Jupyter, Airflow and just worked. Getting started was surprisingly straightforward. We had a functional environment running quickly, without months of onboarding or vendor hand-holding.

What this really means is we kept control. Over cost, over data, over architecture. Long-term ownership vs. convenience, that trade-off is worth thinking about. Ilum makes the choice easier. Review collected by and hosted on G2.com.

You’ll get more out of Ilum if you have an experienced team. It’s not 100% hand-holding you through setup like a managed cloud platform would. That’s fine for us, but it’s worth calling out. You trade polish for total control.

Still, when we hit a wall, the team behind Ilum responded quickly and usually fixed the issue fast. So while there are gaps, they don’t stay gaps for long. Review collected by and hosted on G2.com.

I am impressed with ILUM's seamless setup process. It was really quick to get everything up and running, taking just a few days and even locally on my laptop, it only took about thirty minutes. ILUM serves as our first data platform, effectively functioning as a data warehouse and a data science platform with data ops capabilities. It plays a vital role in our operations, addressing data analytics and data transformation needs comprehensively, from the old-style ETL processes to data visualizations. The most significant advantage of ILUM is that it provides a complete end-to-end platform that can handle a variety of use cases, all within one space, eliminating the need to switch between different tools. I also find the integration options, such as with Power BI, to be quite beneficial. Moreover, ILUM's AI features and data lineage capabilities are excellent. The convenience of having so many diverse tools for data transformation and visualization all in one place is the biggest feature for us. Review collected by and hosted on G2.com.

I think that the only thing that I would change is maybe some white labeling capabilities. Review collected by and hosted on G2.com.

I appreciate that ILUM is a free platform, which makes it the most cost-effective choice for us as a data lakehouse platform. The fact that it integrates seamlessly with our existing systems for data processing, visualization, ETL, and more is invaluable. By handing over the management of tooling to ILUM, we can focus more on data processing rather than on infrastructure, significantly optimizing our workflows. I find the ability to choose where and how to run workloads highly beneficial because it saves us approximately 30% on compute costs. The safety of our data being always in our own hands is reassuring. ILUM manages to support advanced configurations with platforms such as Apache Airflow and integrates well with Apache Spark, which considerably boosts our team's efficiency by enabling autoconfiguration and microservices creation. I admire ILUM's adaptability and its team’s responsiveness; we request features, and the development team quickly implements them. This has led to a platform that grows reliably with our needs, much to our developers' satisfaction, providing them access to cutting-edge tools. Lastly, I value the helpfulness of ILUM’s support team, who assisted us in tailoring the platform to meet our specific needs, ensuring we fully leverage its capabilities. Review collected by and hosted on G2.com.

I found the initial setup of ILUM to be quite demanding due to the heavy reliance on Kubernetes, Apache Spark, Apache Hive, and the need to consult internal documentation. This complexity requires specific knowledge to understand what affects what and how to configure it properly. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Some parts of the interface still feel a bit raw, and the docs could go deeper in a few places. But none of it gets in the way. Ilum does what we need it to do, and these are small things compared to how solid and reliable the core platform is. Review collected by and hosted on G2.com.