Cube Dev
Cube is a universal semantic layer that makes it easy to connect data silos, create consistent metrics, and make them accessible to any data experience your business or your customers needs. Data engineers and application developers use Cube’s developer-friendly platform to organize data from your cloud data warehouses into centralized, consistent definitions and deliver it to every downstream tool via its APIs. Your business data becomes consistent, accurate, easy to access, and, most importantly, trusted. Once trusted, the use of data accelerates throughout your organization, delivering better experiences to your customers and driving intelligence back into the business.
Cube as a complete, universal semantic layer has the following four layers: data model, caching, access controls, and APIs.
Data Modeling:
Data modeling framework is a foundational piece of the universal semantic layer. It helps data teams to centralize data models upstream from data consumption tools, such as BIs, embedded analytics applications, or AI agents. It makes your data architecture DRY by reducing the repetition of data modeling across multiple presentation layers.
Access Control:
One of the benefits of a semantic layer is the active security layer. The semantic layer provides a comprehensive real-time understanding and governance of your data. When all your data consumption tools access data through the semantic layer, it becomes an ideal place to enforce access control policies.
Caching:
The semantic layer can serve as a buffer to the data sources, protecting the cloud data warehouses from unnecessary and redundant load. Caching optimizes performance and can reduce the cloud data warehouse cost.
APIs:
One of the key requirements of the semantic layer is interoperability with data consumption tools: BIs, embedded analytics, and AI agents. The universal semantic layer cannot require one-off integration with every tool, framework, or library. Supporting the ever-growing number of data consumption tools in a one-to-one model is not feasible.
Cube as a complete, universal semantic layer has the following four layers: data model, caching, access controls, and APIs.
Data Modeling:
Data modeling framework is a foundational piece of the universal semantic layer. It helps data teams to centralize data models upstream from data consumption tools, such as BIs, embedded analytics applications, or AI agents. It makes your data architecture DRY by reducing the repetition of data modeling across multiple presentation layers.
Access Control:
One of the benefits of a semantic layer is the active security layer. The semantic layer provides a comprehensive real-time understanding and governance of your data. When all your data consumption tools access data through the semantic layer, it becomes an ideal place to enforce access control policies.
Caching:
The semantic layer can serve as a buffer to the data sources, protecting the cloud data warehouses from unnecessary and redundant load. Caching optimizes performance and can reduce the cloud data warehouse cost.
APIs:
One of the key requirements of the semantic layer is interoperability with data consumption tools: BIs, embedded analytics, and AI agents. The universal semantic layer cannot require one-off integration with every tool, framework, or library. Supporting the ever-growing number of data consumption tools in a one-to-one model is not feasible.
