As the volume of data captured by organizations continues to grow, having a solid foundation that enables teams to manage and scale data operations has become essential. But even best-in-class data management platforms can’t guarantee consistency when defining business metrics across the organization.
To do this, you need a semantic layer—a centralized control plane that allows users to unify business metrics and data sets. A semantic layer provides data teams with consistent metrics and enables all users, regardless of skill level, to easily access and understand their organization’s data.
Coalesce + Cube: Better together
Using a semantic layer on top of your data transformation layer enables the consistent usage of your organization’s data. To help our customers achieve this, we are announcing a strategic partnership and integration with Cube, providers of the universal semantic layer for every data app.
Our new partnership gives customers the building blocks to establish a solid data metrics foundation and take their business analytics to the next level. Integrating Coalesce + Cube into your data stack allows you to seamlessly manage your data pipelines while exposing data models—or cubes—to the semantic layer, instantly giving your data experts a unified view of data with consistent business terminology and metrics.
In this post we’ll explore how adding a semantic layer to your data transformation layer amplifies your entire data analytics experience.
Bring consistency to your metrics
A core component of any strong data foundation is making analytics-ready data models accessible across the organization. While data transformation solutions like Coalesce help you build and scale your data projects, our focus isn’t on building a semantic layer capable of defining metrics and performing other metrics-related governance tasks.
A key advantage of adding Cube to your data stack alongside Coalesce is consistency in your business reporting. Cube allows users to quickly define, manage, and own a single source of truth for business metrics, which get passed to business intelligence solutions such as Tableau, Sigma, and ThoughtSpot. That way, anyone within the organization can view the same metric from the same source, providing parity across all teams for all metrics.
Data teams can develop these metrics in a single application and seamlessly manage semantic layer actions, specifically metric management and definitions. You can then enhance data transformations and models designed in Coalesce with the ability to leverage Cube’s exact metrics and use cases.
Better understand your data
Coalesce and Cube were built with business users in mind. Used together, business stakeholders can easily understand and use the data captured by your organization without technical knowledge of databases or query languages.
Coalesce allows business users to explore automated, real-time documentation and quickly see how objects get created—and their dependencies—while helping them understand column and node descriptions, along with dynamic column-level lineage.
Cube doesn’t require business users to know how to join tables and write complex filters. Instead, they can select definitions and metrics while working with the same data as the rest of the organization. This capability eliminates the all-too-common issue of analysts—and even entire data teams—querying the same data and getting different answers.
Coalesce + Cube provide technical and non-technical business users with the same level of understanding and transparency into the entire data transformation process, including how that data is defined, aggregated, and managed within Cube’s semantic layer.
Empower your data mesh strategy
Coalesce is the best solution for designing a data mesh architecture, and our integration with Cube takes the data mesh experience to the next level.
While Coalesce helps data practitioners focus on building domain-driven products and provides democratized access to data projects, Cube allows these same people to work with analytics-ready data without the need for support from the other data or domain experts. Instead, they can use definitions and pre-aggregated data within Cube to immediately leverage the same data as the rest of the organization, allowing every team to work independently using the same data products.
Optimize your analytics
Because Coalesce provides a first-class data foundation, using complementary solutions that allow you to work quickly and with the same quality as your Coalesce models is crucial.
Cube enables self-service analytics and bypasses the need to create complicated joins, relationships, or additional data preparation tasks. Using Cube on top of Coalesce helps simplify these queries and provides predefined metrics that expedite your analytics process.
Additionally, when you pre-aggregate data in Cube, you can reduce the load on your data platform by providing smaller, aggregated data sets—meaning you optimize compute once with Coalesce and again by using a semantic layer in Cube to improve query performance.
Level up your data stack
Data maturity comes from consistent metrics that amplify your organization’s ability to consistently analyze data, make faster decisions, and agree on what metrics mean.
The new integration between Coalesce and Cube gives customers all the advantages of a best-in-class data transformation solution and the power of a comprehensive semantic layer. To get started, you can immediately begin using Cube’s semantic layer with Coalesce data transformations by setting up an integration within any Cube environment.
Learn more
- Join us live for Coffee with Coalesce: Semantic Layer Innovation with Cube
- Attend Demos with Doug: Peeling Back Semantic Layers with Coalesce and Cube
- Book a demo to see the power of Coalesce + Cube in action today
