Data teams have delivered sub‑second email metrics and have secured app access to analytics
What is our primary use case?
We needed Cube in order to have a robust semantic layer on top of our ClickHouse database to avoid exposing our projection database directly in our app, and we needed to have sub-second latency metrics for our users.
We directly embed the queries generated by Cube in our app.
Our software engineering team provides the data team with events coming from ClickHouse. We ingest this data and enrich it with other sources, which allows us to create new dimensions and measures that should be displayed in our app. We did not want to expose our data warehouse or our production database directly in the app, so we use Cube to generate JavaScript queries and put them directly in our customer's app. A specific use case is for our deliverability team, which provides our clients with metrics about email deliveries.
What is most valuable?
Cube is a robust semantic layer that is really helpful in converting SQL queries into JavaScript functions, and it integrates smoothly in our single page framework application.
Among others, I would highlight the ability to convert SQL queries into JavaScript functions easily, as well as the cache feature that is really helpful in managing our resources.
For the deliverability team use case, we needed to have almost real-time data displaying at sub-second latency. We are using the cache feature that stores the results every five minutes. This allows us to have almost real-time metrics while managing our resources efficiently at scale.
What needs improvement?
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.
For how long have I used the solution?
We have been using Cube for roughly one year and a half.
What do I think about the stability of the solution?
Cube is definitely stable.
What do I think about the scalability of the solution?
Cube is really scalable. The one thing I did not test so far is the way it handles nested fields, but I am sure it is doing so properly. If it handles this kind of thing, in the future we could get rid of Omni and maybe switch to Cube fully.
How are customer service and support?
We did not really have to deal with customer support since we implemented it internally with on-premises.
Which solution did I use previously and why did I switch?
We do not have previous solutions for this specific use case, but we have plenty of different solutions. We have ClickHouse, and we have data exposed through another semantic layer called OmniVision and OmniAnalytics, which is directly plugged into our data warehouse on BigQuery. In app, we have three different sources exposed: one directly from ClickHouse for main dashboards, one from Cube directly, and one from Omni semantic layer for self-service analytics.
We considered using the semantic layer of Omni, but it is actually really expensive. We needed to separate our use cases. One thing that would be really helpful using Cube would be to have the ability to generate charts directly and embed them inside our app. I would say we could quit Omni for this kind of feature.
How was the initial setup?
Implementation was super smooth. Within two weeks, we were up and running and the metrics were exposed in our app. We also really enabled a team within the company that was not able to play with data and expose it to the client, especially since this is a very niche team inside the company. We could not measure the ROI per se, but our ideal customer profile and targeted clients were really amazed by this because it was providing them with the way our server is running for them to handle their marketing campaigns. It is more for advanced users, but it is really important for them because they need to see the ROI in everything they pay for.
What other advice do I have?
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.
One thing that would be really helpful using Cube would be to have the ability to generate charts directly and embed them inside our app. I would say we could quit Omni for this kind of feature.
I would recommend starting with a use case directly, using Docker, and putting it up and running really quickly. Then plug a source. Do not plug many sources at the very beginning. Just try it, check the value proposition, and I am pretty sure you will be amazed in no time. Identify a pain point and try to tackle it with Cube. Once you have done this step, you are pretty much committed to the solution because it works. I would rate my overall experience with this solution as nine out of ten.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Planning has become faster and reporting has improved for complex financial projects
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have used Cube for around three years.
What do I think about the scalability of the solution?
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.
How are customer service and support?
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.
Which solution did I use previously and why did I switch?
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.
What was our ROI?
I did not see any return on investment from Cube.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
LookML is also an option similar to Cube. Another option I evaluated was Looker.
What other advice do I have?
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.
Unified metrics have reduced support tickets and now provide faster, trusted customer analytics
What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Cube for approximately one year and a half.
What do I think about the stability of the solution?
Cube is very stable. We have never experienced any issues.
What do I think about the scalability of the solution?
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.
How are customer service and support?
We have limited customer support interactions, mainly utilizing Cube's Slack community for inquiries.
Which solution did I use previously and why did I switch?
We did not have any solution before. We did not have a semantic layer before implementing Cube.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
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.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Automated reporting has freed time for deeper analysis and improved budget and variance reviews
What is our primary use case?
Cube is the best absolute best FP&A software, dollar for dollar out there. My organization looked at a few different tools and none of them came close to Cube in terms of the value that we get from it now. We really wanted three different things for our organization: automated financial reporting, ease of financial review, and assistance with budget and flux models. Cube was the only software that really let a bunch of us non-technical users at my organization accomplish all of our goals without sacrificing anything.
Cube easily integrates into Excel and makes it simple for us to plug it right into our template and roll it forward. Our FP&A team has been able to utilize this software exceptionally well. Their forecasting and budgeting has been top-notch and faster.
Regarding how Cube fits into my workflow, it is extremely simple to set up and easy to run. The website portal is very clean and well-organized, making it possible to create forecast or budgeting scenarios with just a click of a button.
What is most valuable?
A specific example of how my team uses Cube in our day-to-day work is that above all, Cube has vastly enhanced our ability to get financial reporting done quickly and free up our time to really dig deep into various accounts. This has greatly improved the accuracy of our financial results beyond what you would even believe.
The clean portal and organization help my team by making it easy to navigate and the data collected is very clean and managed in an understandable manner, hence making it very easy to make data-driven decisions.
Regarding the features, customer service is great, customization of financial reports, ease of integration with other tools seamlessly, continuous system testing and upgrades, and easy creation of monthly and P&L variance analysis. Data import and export is smooth and efficient. Monthly reporting and analysis is easy to pull and update.
The positive impact Cube has had on my organization includes additional time for analysis, less than budgeted spend, and more accurate financial results resulting in better decisions. The error rate has reduced from 40 to 50%.
The reduction in errors has affected my team and the business overall by improving speed and efficiency for month-end close processes. Better consolidation of data for long-term trend analysis is evident, and easy P&L creation and variance analysis has been great.
What needs improvement?
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.
For how long have I used the solution?
I have been using Cube for four years and a few months.
What do I think about the stability of the solution?
Cube is stable as I have not experienced any downtime or logging issues.
What do I think about the scalability of the solution?
Cube's scalability is very good because it handles my organization with great efficiency.
How are customer service and support?
Customer support for Cube is very responsive and solution-oriented.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I previously used Workday Adaptive Planning and I switched to Cube because it lacks a lot of the features that Cube provides.
How was the initial setup?
I chose a nine out of 10 for Cube because it is very easy to use even for us non-technical users. It has a very intuitive, user-friendly interface. It has helped us improve speed and efficiency for month-end close processes. We have gotten better consolidation of data for long-term trend analysis. The setup is very easy and required almost no help from IT, which is a big plus.
Cube's ability to create custom reports easily on the fly is impressive. It is fully Excel-based and simple to use. Integration was extremely simple. It is simple to create custom reports on the fly, and easy to review financial performance by each department, which enables greater transparency in departmental-level budgeting.
What was our ROI?
I have seen a return on investment as Cube has streamlined the creation of monthly close packs, helped business partners better understand their monthly P&Ls, and allowed for more granular BVA reporting.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is that the price is very cost-effective and licensing is very affordable, making it a great financial reporting tool for startups.
Which other solutions did I evaluate?
Before choosing Cube, I evaluated other options, including OnePlan.
What other advice do I have?
Cube is well-suited to help save time on financial reporting if you need to refresh the same templates each month. It is also great for building templates and pulling in data directly from Excel or Google Sheets. However, it is less appropriate for companies that run into issues with uploading large sets of transaction data, and it does not have robust planning or forecasting features that you will find in other FP&A tools competitors. I would rate this product a 9 out of 10.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Powerful Semantic Layer, Poor Cloud Support
What do you like best about the product?
I find Cube to be a very sophisticated product that allows me to design complex metrics and dimensions in a scalable and composable way. I also appreciate the useful caching (preaggregation) layer. Having a strong common semantic layer is key for us as a data product company serving dashboards and insights to our customers.
What do you dislike about the product?
The service provided by the cloud team is horrible. We are a paying customer, with an annual commitment, and are having big issues when scaling that are not being solved by them. We don't have visibility into what is happening.
What problems is the product solving and how is that benefiting you?
Cube closes the gap between our data model and business metrics, supporting complex metric design with a scalable semantic layer and a useful caching layer.
Super Easy Access to the DEV team
What do you like best about the product?
Cube is a platform that presents the data in a very clear way. When we do the selection of column, it can also automatically generate sql code for SSMS. The query history is also great for tracking the error. When there's any problem, cube dev team is super easy to access, and they respond very fast. They really provide a strong tech support.
What do you dislike about the product?
Sometimes they query history loaded longer than expected.
What problems is the product solving and how is that benefiting you?
It can show data very clearly and allow me to select any column to see the data in an easy way.
Great for AI applications and general data apps!
What do you like best about the product?
I'm using CubeJS for a lot of data apps on my company and it is really incredible for leveraging data for my applications. I also use CubeJS daily for manual analytics, like taking a look at the charts or copying some data to perform some stronger statistical analysis.
The interface simplicity of consumption for REST APIs is really powerful for integrating in any language and maintaining a standardized consumption around the tech ecosystem.
This can also be used as a "Feature Store" of some sorts, together with the preAggregations layer, for ML and AI applications, making it really really simple to roll them out.
I've already used the Customer Support and their response was really fast and the person helping me was also really proactive.
The ability to test changes to a cube or creating a cube via Git branches is **really powerful**! It really helps!
Finally, implementing cubes, measures, and also configuring dimensions is way too simple: we use it to connect to BigQuery and maintaining the semantics of metrics on CubeJS, which makes it so easy to understand data.
What do you dislike about the product?
I still think documentation is a bit confusing for some features.
I've faced some issues with preAggregations where some measuers were they were being summed instead of being averaged over for `avg` types. Some logic for the `rollup` timeout is a bit confusing.
Although the REST API interface doens't really need any framework, I feel that a simple SDK providing the objects for serialization would be nice to have.
Some more complex charts in the Playground would also be nice to have, together with a simple export like CSV/JSON from the Playground (I do understand that the Playground is... a Playground... but sometimes I use it as an analytics tool to fetch some informations).
I think that we also lack a propert LSP (or a type checker) for Cube on Javascript. Sometimes I makes some mistakes that are related to Cube JS syntax (use of AI on the Code Editor also makes it more prone to error). This would be really helpful. Some features can only be tested once in production (like preAggregations syntax), which makes it a bit harder to integrate.
What problems is the product solving and how is that benefiting you?
CubeJS helps me solve the issue of integrating data from multiple sources to Applications: from general software to AI-powered applications, CubeJS allows us to have low latency data fetching and only worrying with what to do with the data.
What a modern semantic layer solution should look like
What do you like best about the product?
Using CubeJS Cloud to build our semantic layer has been great! It really just takes a lot of development effort off our shoulders. This is especially great for small teams that don't have DevOps engineers available to help create and monitor a platform. The development and maintenance have been simple to maintain. Their support is also great where I could solve a build issue with a live chat in a short time. There are many features including different endpoints to consume data, which makes embedding analytics into our product straightforward.
What do you dislike about the product?
I still feel that integration with BI tools is not totally straightforward yet, some query aggregations fail and require some tweaks. I still have to test the new "Semantic Layer Sync" feature. Also, a thing that was very great about dbt metrics that I miss here in CubeJS is the ability to create a new metric based on other metrics as long as they have the same time granularity, CubeJS could find a way to implement something flexible as this as well.
What problems is the product solving and how is that benefiting you?
We use all major features: serving metrics on endpoints, embedded analytics in our product front-end, and feed BI tools.
Easy to setup analytics
What do you like best about the product?
- declarative schema definitions make it easy to built UI interfaces on top of your data
- built-in cache
- extensibility through express middlewares
- great slack community
- easy to get setup
What do you dislike about the product?
we ran into an issue with cube cloud processes restarting too frequently which meant the entire schema needed to be reloaded on subsequent requests which took nearly 5s and triggered our latency alarms. luckily due to cube's extensibility we were able to implement a custom express middleware to trigger loading of the schema when the instance started up which worked around the problem for us.
the schema compiler is a little quirky and has too much magic which makes it a little more difficult to define schemas in typescript, but not insurmountable.
personally I don't like the online schema editor since we discourage folks from making changes outside of the version control. I wish we could have disabled this feature.
out-of-the-box scripts to scaffold out schemas would be nice. we ended up rolling our own.
cube-sql is interesting, but the lack of ability to do basic math in queries made it unusable for us.
What problems is the product solving and how is that benefiting you?
Cube makes it easy to build analytics tools on your data.
Great Data-Driven Applications
What do you like best about the product?
Cube.js has been a game-changer in my development journey, offering an incredible solution for building data-driven applications. With its intuitive data modeling capabilities, I've been able to structure data in a way that truly suits my application's needs. it handles data efficiently, ensuring that my users can explore insights seamlessly.
What do you dislike about the product?
Cube.js, like any tool with advanced capabilities, has a learning curve. Beginners may find it challenging to grasp all the concepts, especially when dealing with complex data models and SQL queries. However, Cube.js does provide documentation and tutorials to help mitigate this issue.
What problems is the product solving and how is that benefiting you?
Cube.js simplifies complex data modeling by providing a structured way to define cubes, dimensions, and measures. This makes it easier to represent and work with your data, allowing you to create meaningful analytics without getting lost in complex SQL queries or data transformations.
