Data Scientist / Data Analytics Consultant at Fiverr International Ltd
Consultant
Top 10
Jul 14, 2026
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.
Senior Analytics Engineer at a tech vendor with 501-1,000 employees
Real User
Top 5
May 12, 2026
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.
Project manager at a consultancy with 51-200 employees
Real User
Top 10
May 6, 2026
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.
Analytics Engineer at a tech vendor with 501-1,000 employees
Real User
Top 5
May 6, 2026
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.
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.
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...
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.
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 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 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.
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.