Data Scientist / Data Analytics Consultant at Fiverr International Ltd
Consultant
Top 10
Jul 14, 2026
To evaluate Cube's efficiency in handling large data volumes, I use metrics including F1 scores, AUC (Area Under the Curve), and RMC (Root Mean Squares), along with RSE (Root Square Errors). These factors help me gauge the efficiency of the algorithms clearly. Regarding improving Cube, I believe that when we are building hierarchies, I should enhance the type of hierarchies. For instance, in a few countries lacking state systems, there could be a hierarchy without proper categorization—such as continent, country, and city. Therefore, it would be beneficial if I could improve the hierarchies to retrieve data as quickly as possible from Cube.
Senior Analytics Engineer at a tech vendor with 501-1,000 employees
Real User
Top 5
May 12, 2026
There is something that should be improved. We are providing metrics on email, and in the email industry we have both transactional emails and marketing emails. We have different models for these, but the metrics are actually the same: open rates, deliverability rates, soft bounce rates, and other metrics. There is no way to create a real template that is not exposed directly in the UI. We basically customized it by creating a file for all the metrics, and then we extended our previous views with this template. However, this template is exposed directly in the UI, which is not relevant for us. We do not want people using the UI and selecting metrics from this template. For the UI, our use case is more for back-end engineering, so not everyone using it is using the UI. Something that could be really helpful when using the UI is the ability to make it nicer and more intuitive. To illustrate what I am saying, we cannot order the fields in the UI. We cannot say that we want organization ID to be on top. It is going to be sorted alphabetically, and I do not think that is the most practical way to manage everything, especially when we have views with roughly one hundred dimensions and of course some measures as well.
Project manager at a consultancy with 51-200 employees
Real User
Top 10
May 6, 2026
Everything is functioning well, but Cube is a little bit slow when I use multiple projects at the same time, which makes it very hard to run. I would appreciate if Cube could be more human accessible, as there is no free access available. I am not getting access to the knowledge center.
Analytics Engineer at a tech vendor with 501-1,000 employees
Real User
Top 5
May 6, 2026
Cube's interface can be challenging for non-technical users, needing clearer use-case examples to ease integration into workflows. Despite AI introductions, deterministic outputs require better contextual understanding of company needs. Cube's SQL API, while useful, sometimes struggles with complex BI-generated SQL. Enhancements in SQL pass-through could alleviate occasional issues, such as timeouts and CPU impact when handling advanced functions in TRIM or window functions.
Cube can be improved by enhancing data refresh over multiple tabs. The speed at which data is imported can also be improved. Additionally, Cube needs to add functionality for headcount planning.
Cube offers a dynamic business intelligence platform tailored for efficient data transformation and analytics. Engineered for scalability and performance, Cube adapts to complex data environments, enhancing data accessibility and operational insights.Cube facilitates seamless integration into existing data ecosystems, bringing enhanced data processing capabilities to businesses. Utilized by companies seeking streamlined analytical processes, Cube's architecture supports custom data...
To evaluate Cube's efficiency in handling large data volumes, I use metrics including F1 scores, AUC (Area Under the Curve), and RMC (Root Mean Squares), along with RSE (Root Square Errors). These factors help me gauge the efficiency of the algorithms clearly. Regarding improving Cube, I believe that when we are building hierarchies, I should enhance the type of hierarchies. For instance, in a few countries lacking state systems, there could be a hierarchy without proper categorization—such as continent, country, and city. Therefore, it would be beneficial if I could improve the hierarchies to retrieve data as quickly as possible from Cube.
There is something that should be improved. We are providing metrics on email, and in the email industry we have both transactional emails and marketing emails. We have different models for these, but the metrics are actually the same: open rates, deliverability rates, soft bounce rates, and other metrics. There is no way to create a real template that is not exposed directly in the UI. We basically customized it by creating a file for all the metrics, and then we extended our previous views with this template. However, this template is exposed directly in the UI, which is not relevant for us. We do not want people using the UI and selecting metrics from this template. For the UI, our use case is more for back-end engineering, so not everyone using it is using the UI. Something that could be really helpful when using the UI is the ability to make it nicer and more intuitive. To illustrate what I am saying, we cannot order the fields in the UI. We cannot say that we want organization ID to be on top. It is going to be sorted alphabetically, and I do not think that is the most practical way to manage everything, especially when we have views with roughly one hundred dimensions and of course some measures as well.
Everything is functioning well, but Cube is a little bit slow when I use multiple projects at the same time, which makes it very hard to run. I would appreciate if Cube could be more human accessible, as there is no free access available. I am not getting access to the knowledge center.
Cube's interface can be challenging for non-technical users, needing clearer use-case examples to ease integration into workflows. Despite AI introductions, deterministic outputs require better contextual understanding of company needs. Cube's SQL API, while useful, sometimes struggles with complex BI-generated SQL. Enhancements in SQL pass-through could alleviate occasional issues, such as timeouts and CPU impact when handling advanced functions in TRIM or window functions.
Cube can be improved by enhancing data refresh over multiple tabs. The speed at which data is imported can also be improved. Additionally, Cube needs to add functionality for headcount planning.