Semantic Layer

Every Data Stack Needs a Semantic Layer

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The problem:
Decentralized data models, data definitions, and controls over data access create data chaos

The average enterprise uses 6+ BI tools and has 100s of data sources. Each has their own access control and data modeling, they don’t communicate or agree with each other and cause your team to distrust the data.

The solution:
Centralize with a semantic layer and rebuild trust

Like a centralized control panel for your company's data – A semantic layer manages data modeling, data access, and sends consistent data and metrics to every BI software, data app, and AI/LLM tool.

Cube Cloud: hosted, optimized, enterprise-ready

All the features of Cube (data modeling, data access control, caching, and a slew of APIs) with added observability tools, developer toolkits to accelerate idea-to-delivery, enterprise compliance and certification, as well as a host of features to love.

Data Modeling

Define and manage metrics upstream to consolidate your workflow, centralize definitions, and create a single source of truth.

Access Control

Grant column- and role-based operational and viewing permissions upstream with granular access controls.

Caching

Ensure your data is reliably performant with a powerful caching layer and advanced preaggregation capabilities.

APIs

Connect your data to any front-end application to build beautiful custom visualizations with Cube’s GraphQL, REST, and SQL APIs.

GIGAOM SONAR REPORT

“Semantic layers are being recognized as independent components of organizations’ portfolios of data technologies.”

The GigaOm Sonar report on Semantic Layers dives deep into the latest trends, innovations, and key players shaping the future of Semantic Layers in the data stack. But its conclusions are clear: “semantic layers are beneficial for any use case that might require a higher-level, consolidated view of an organization’s data” And “The industry is rediscovering the benefits that defining the semantic model up front can have on ease of adoption and consumption, query performance and speed, consistency, data democratization, and governance.”

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SEMANTIC LAYERS IN THE WILD

Cloud Academy delivers insights 70% faster with a semantic layer - all while maintaining control over data access

After finding success delivering customer-facing analytics using Cube Cloud’s semantic layer, Cloud Academy realized that it could also provide the security orchestration needed to deliver data to internal stakeholders on how their product is being used: course consumption, learning paths, and key metrics related to the recommendation system. And everyone wanted access to these insights: product, marketing, sales, executives, and other teams that wanted to leverage this data to make key business decisions.

Speed + Performance

Essential features every semantic layer must have

A semantic layer must accelerate the speed of data modeling through developer tools like playgrounds and automatic testing, easily manage data access to a granular row-level control, as well as offer a plethora of APIs to deliver those metrics and data to many different data visualizations. In addition, the best semantic layers, Cube Cloud included, will allow for pre-aggregations to make it simple to speed up the experience of your analytics for users.

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Cube's strengths include its strong code-first orientation, native API support, and its analytics pre-processing through caching and pre-aggregations.