Cube Reviews

In my recent projects with Cube, I was tasked with finding crashes and the reasons behind them using three datasets: people, crash, and vehicles. I had to merge them and preprocess them, clean and fill the missing values and outliers, and then divide the datasets into the star schema. After filling the data from local sources, I uploaded the data to the server and ran Cube. We do this to retrieve data by creating hierarchies; for example, within a city, we can specify particular areas and then drill down. When we fetch one thing, we connect with the hierarchy to retrieve the latest part of the data.

I have recently worked with two tools related to Cube: SSIS and SSAS, which are SQL Server Integration Service and Analytical Service. It is important when we build the pipelines and make the checks to insert new data into the pipelines, validating any null or missing values that might still be there. To make the pipelines efficient, we have checks in the pipeline that enhance the efficient features for Cube as well as the keys.

Cube's caching mechanism impacts my database query loads and response times significantly. Once we create the star schema and Cube, we do this to retrieve specific data as quickly as possible because with billions of records, fetching everything takes a lot of time. We establish specific measures and counts and design a specific star schema. Based on that, we use fact tables and dimension tables to fetch records as quickly as possible.

When it comes to Cube's multi-tenant features, I need to observe the requirements of the data when it is available across different clouds, including Google GCP and AWS. If I need something that exists on both clouds, I have to fetch the specific data based on these requirements using data analytics queries and analytical queries.

Regarding Cube's dynamic access controls, I have to keep in touch with the compliance departments to control data security and inform them about everything I have. This includes encrypting the data to make it more efficient. Sharing everything with the compliance departments adds security features that make the dynamic data more secure and efficient. I understand that data is vital, and I need to ensure it remains confidential.

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.

I have been working with Cube for two and a half years. I am still working on many projects, and I have worked on many datasets, including vehicle crash results and HR-related datasets. I am still collaborating with professors on my Tech Well Group. I recently finished my internship from there as well. I am familiar with many of these aspects.

Regarding Cube's technical support, I have discussed issues with my professors whenever I faced technical difficulties. They provided guidance by suggesting possible troubleshooting methods to help overcome the technical issues I encountered.

When it comes to the initial setup of Cube, I faced some challenges, including server issues when uploading data from local sources to the Unipi servers. Most of the time, I encounter connectivity errors, but I navigate these issues by ensuring I match IDs and records and align the columns of the tables I have created in the data warehouse with those in local CSV files. Once everything is matched, I can transfer data to the data warehouse and construct the schemas. These are the common challenges I face while developing Cube, yet I still manage to proceed.

When considering other options in the market, I find that I make Cube when handling a large amount of data. I create Cube for specific segments of data. For example, if I manage one million records, I focus on that critical data which I will use most often. This is why I create Cube for that one million records, making it easier and faster to retrieve and validate the data.

I have not utilized Cube's pre-aggregation feature, but I have studied how to build decision trees from parent to child leaves. I understand how it is structured with two branches, three branches, and so on. I have worked on that aspect as well concerning decision trees.

From the functionality perspective, at this moment, I do not think there are any missing features in Cube that I would want to see included in the next release. Perhaps some features could be added depending on specific working conditions.

Given my experience with Cube, I would advise organizations to prioritize managing hierarchies when developing Cube, as hierarchies are vital for efficient data retrieval. The hierarchy resembles a tree structure with parent, child, and then child leaf, and it essentially continues. If I manage these hierarchies effectively, it greatly facilitates retrieving the specific data and records I need in an efficient and timely manner. Overall, I would rate this product a 7.5 out of 10.

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.

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.

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.

I have been using Cube for four years and a few months.

Cube is stable as I have not experienced any downtime or logging issues.

Cube's scalability is very good because it handles my organization with great efficiency.

Customer support for Cube is very responsive and solution-oriented.

I previously used Workday Adaptive Planning and I switched to Cube because it lacks a lot of the features that Cube provides.

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.

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.

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.

Before choosing Cube, I evaluated other options, including OnePlan.

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.

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.

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.

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.

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.

We have been using Cube for roughly one year and a half.

Cube is definitely stable.

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.

We did not really have to deal with customer support since we implemented it internally with on-premises.

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.

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.

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.

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.