Cube Cloud

Cube  is an end-to-end digital move technology and solution that I have used in the digital era. I run various projects such as market research surveys, which are end-to-end projects where I use Cube  for modeling and designing purposes.

Cube helps me significantly with financial planning and analysis. The tool creates spreadsheets for financial planning analysis, and there is an option for a repository of financial operation data. This allows my team to build faster, more accurate scenarios and reports.

Cube completes my tasks very easily and takes less time, allowing me to deliver any project in a timely manner to our clients. Cube has definitely helped me a great deal.

I was running a financial project where my team was taking too much time, and when I ran that project on Cube, it helped me move from manual data management to strategic analysis very easily.

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.

I have used Cube for around three years.

Cube only supports daily use. If I increase the workload, then it will not work as efficiently as I would want it to per my expectations.

I do not have any experience with customer support for Cube, so I am not going to share any opinion on customer support. Some colleagues told me you can use different tools as well.

I did not use any different solution before Cube, so I do not have much idea. However, when I used traditional BI, it was the same.

I did not see any return on investment from Cube.

The cost is around $1,500 per month. The exact number is not coming to my mind, but it is approximately $1,500 or $200 per month.

LookML is also an option similar to Cube. Another option I evaluated was Looker .

My experience with pricing, setup cost, and licensing for Cube was good and it helped me a great deal in analyzing the pricing. I believe Cube's relationship with my company is as a reseller. My overall review rating for this product is 8.

Cube  is used at Brevo  to expose customer-facing analytics in the product. The DBT semantic layer proved effective for internal BI, but for customer-facing analytics, a high-concurrency app was needed. Cube  was ideal for defining a single source of truth, queryable via API with rapid response times thanks to Cube Store and caching.

Example use cases include an emailing analytics portal offering insights into deliverability metrics, such as hard bounce rates. This metric is defined in Cube and is calculated by dividing the sum of delivered emails by those with a hard bounce event. Governance  of metrics is crucial for consistency across the product, reducing discrepancies and ensuring everyone is aligned.

Key to this strategy is having versioned metrics governed by GitHub , offering transparency and impact analysis when changes occur, aligning communications with the backend development team on a unified front.

The first major impact observed was a reduction in support tickets. Internally, uncertainty around metric definitions was resolved, aligning everyone. Additionally, a customer NPS  survey showed high satisfaction with data quality and bug reduction. Previously, discrepancies between UI reports and analytics caused customer frustrations and increased support tickets.

NPS  improved to approximately eight out of ten for our feature, and internally ticket handling times decreased, allowing reallocation of resources to higher-impact projects. Financially, less churn on customer analytics offers has also led to more revenue and an overall positive ROI.

Cube's standout features include assisted modeling with AI for quicker onboarding, saving time in developing the semantic layer. Cube's semantic layer centralizes a single source of truth for metrics, preventing data drift, heightened by version control. Additionally, Cube's pre-aggregations and Cube Store boost query performance for significant data sets, offering rapid results, crucial for user experience.

The performance of the tool with pre-aggregation is excellent, providing fast response times and reliable metric governance. AI capabilities enhance the developer experience, and its robust features markedly impact business operations.

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.

I have been using Cube for approximately one year and a half.

Cube is very stable. We have never experienced any issues.

Our customer analytics software scales excellently, managing thousands of customer requests per second without issues. Cube's API is robust, with multiple Cube API and refresh worker instances managed behind a load balancer, supporting organizational scalability by dividing into microservices.

We have limited customer support interactions, mainly utilizing Cube's Slack community for inquiries.

We did not have any solution before. We did not have a semantic layer before implementing Cube.

It is difficult to quantify exactly, but less churn on our customer analytics offer means more revenue. Additionally, reduced ticket times allow staff reallocation to impactful projects, signaling positive ROI.

We do not have much experience because we are using the open source version. As we are hosting it ourselves, we do not pay much.

I am aware of alternatives in the DBT semantic layer. Cube was chosen due to its caching and Cube Store capabilities essential for customer-facing dashboards. No other solutions were evaluated once Cube's benefits were validated in a DBT community article.

Understanding Cube's capabilities and adapting organizational data philosophies are imperative. Initial adoption should focus on building a minimum viable semantic layer, consolidating key metrics into a single source of truth to showcase the tool's value. Budget constraints dictate the choice between open source or cloud implementations. I would rate this product an eight out of ten.