About Cube

Cube offers a universal semantic layer that standardizes data definitions and governance across multiple business intelligence tools, enabling consistent insights and efficient analytics workflows. By centralizing data modeling and access control, Cube reduces analytics downtime and accelerates the development of data applications, resulting in significant cost savings and improved performance.

```xml <problem> Data silos and inconsistent business definitions across various business intelligence (BI) tools lead to fragmented analytics workflows and a lack of a single source of truth. This results in duplicated data modeling efforts, increased analytics downtime, and difficulties in ensuring consistent insights for informed business decisions. </problem> <solution> Cube offers a universal semantic layer that sits between data sources and data consumers, providing a centralized platform for data modeling, access control, caching, and API integration. It unifies fragmented business definitions by consolidating data modeling workflows, ensuring consistent metrics across all BI platforms and data endpoints. Cube enables centralized enforcement of fine-grained governance and security policies, granting row and column-level permissions and masking sensitive data upstream. Its caching layer optimizes query performance and reduces cloud costs, while its AI, GraphQL, MDX, REST, and SQL APIs facilitate integration with any endpoint, delivering trusted data to front-end applications and AI agents. </solution> <features> - Code-first approach to data modeling using YAML or JavaScript, enabling Git flow for managing changes and isolated environments. - Dataset-centric data modeling framework with cubes representing business entities and views creating facades for data consumers. - Pre-aggregations framework for caching, building, and refreshing rollup tables to speed up queries and reduce cloud data warehouse costs. - Comprehensive access control policies, including row-level and column-level security, defined using Python or JavaScript. - Support for REST, GraphQL, and SQL APIs for interoperability with various BI tools, embedded analytics, and AI agents. - Semantic Layer Sync for integration with BI tools like Apache Superset, Metabase, Preset, and Tableau. - Orchestration API for integration with Airflow, Dagster, and Prefect. </features> <target_audience> Cube is designed for data engineers and application developers who need to organize data from cloud data warehouses into centralized, consistent definitions and deliver it to various downstream tools. </target_audience> <revenue_model> Cube Cloud offers tiered pricing based on Cube Consumption Units (CCUs), with options ranging from a free Starter plan to customized Enterprise and Enterprise Premier plans with varying levels of support, uptime SLAs, and features. </revenue_model> ```

What does Cube do?

When was Cube founded?

Cube was founded in 2019.

How much funding has Cube raised?

Cube has raised 25000000.

Founded

2019

Funding

25000000

Employees

68 employees

Major Investors

Databricks Ventures

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Cube

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Executive Summary

cube.dev 5K+

Crunchbase

Founded 2019

Funding

Estimated Funding

$20M+

Major Investors

Databricks Ventures

Team (50+)

No team information available.

Company Description

Problem

Data silos and inconsistent business definitions across various business intelligence (BI) tools lead to fragmented analytics workflows and a lack of a single source of truth. This results in duplicated data modeling efforts, increased analytics downtime, and difficulties in ensuring consistent insights for informed business decisions.

Solution

Cube offers a universal semantic layer that sits between data sources and data consumers, providing a centralized platform for data modeling, access control, caching, and API integration. It unifies fragmented business definitions by consolidating data modeling workflows, ensuring consistent metrics across all BI platforms and data endpoints. Cube enables centralized enforcement of fine-grained governance and security policies, granting row and column-level permissions and masking sensitive data upstream. Its caching layer optimizes query performance and reduces cloud costs, while its AI, GraphQL, MDX, REST, and SQL APIs facilitate integration with any endpoint, delivering trusted data to front-end applications and AI agents.

Features

Code-first approach to data modeling using YAML or JavaScript, enabling Git flow for managing changes and isolated environments.

Dataset-centric data modeling framework with cubes representing business entities and views creating facades for data consumers.

Pre-aggregations framework for caching, building, and refreshing rollup tables to speed up queries and reduce cloud data warehouse costs.

Comprehensive access control policies, including row-level and column-level security, defined using Python or JavaScript.

Support for REST, GraphQL, and SQL APIs for interoperability with various BI tools, embedded analytics, and AI agents.

Semantic Layer Sync for integration with BI tools like Apache Superset, Metabase, Preset, and Tableau.

Orchestration API for integration with Airflow, Dagster, and Prefect.

Target Audience

Cube is designed for data engineers and application developers who need to organize data from cloud data warehouses into centralized, consistent definitions and deliver it to various downstream tools.

Revenue Model

Cube Cloud offers tiered pricing based on Cube Consumption Units (CCUs), with options ranging from a free Starter plan to customized Enterprise and Enterprise Premier plans with varying levels of support, uptime SLAs, and features.