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Cube

Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools.

The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orchestrates these interactions by mapping questions to the underlying semantic model, ensuring that AI-generated insights remain accurate and context-aware. Furthermore, Cube is designed for multi-tenant environments, offering robust infrastructure isolation, row-level security, and dynamic context injection to ensure that data access is strictly governed and personalized for every user or tenant.

Beyond its core modeling and AI features, the platform includes a comprehensive suite of tools for performance optimization, including automated pre-aggregation caching and asynchronous query queuing. It supports a wide range of data sources and deployment models, from self-hosted containers to managed cloud environments. The system also provides extensive programmatic control over report management, dashboard publishing, and user identity synchronization, making it suitable for embedding interactive analytics directly into custom software applications.

Features

  • AI Query Orchestrators - Maps natural language questions to semantic model members and trusted SQL patterns to generate accurate, context-aware analytical insights.
  • Natural Language Querying Interfaces - Translates natural language questions into analytical insights by leveraging a semantic data layer.
  • Business Intelligence Platforms - Provides an API-first analytics engine delivering structured data via SQL, REST, and GraphQL.
  • Data Modeling Frameworks - Provides a code-first environment for defining metrics, dimensions, and relationships across disparate data sources to ensure a single source of truth.

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  • Metric and Dimension Configuration - Defines the core business metrics and dimensions that power the semantic data layer.
  • Semantic Data Models - Translates raw database schemas into reusable business entities using a unified configuration syntax for consistent analytical definitions.
  • Embedded Analytics Solutions - Integrates interactive dashboards and conversational analytics directly into custom software applications.
  • AI-Powered Data Assistants - Deploys conversational agents that use a semantic model to provide trusted, context-aware answers to natural language queries.
  • Conversational AI Assistants - Enables natural language data exploration by connecting AI agents to a semantic layer for trusted, context-aware business insights.
  • Relationship Aggregators - Computes summary values using standard aggregations, conditional filters, and mathematical formulas to derive business insights.
  • Data Models - Maps database tables to logical objects containing business logic and relationships.
  • Metric Calculators - Defines measures to perform counts, sums, averages, and custom calculations over datasets for reporting.
  • Row Level Security - Filters query results at the row level based on user roles to ensure authorized data access.
  • Data Access Filters - Applies row-level filters and column-level constraints to queries based on user claims to ensure authorized data access.
  • Agent Access Controls - Limits the specific views accessible to automated agents within the semantic model to control data interaction scope.
  • Agent Definitions - Encodes business definitions and guidelines as version-controlled prompts for AI agents.
  • Agentic Context Management - Provides isolated memory and knowledge scopes to ensure agents only access information relevant to their assigned team or function.
  • AI Agent Builders - Allows deployment of specialized agents with distinct instructions, tool access, and isolated data contexts for different teams.
  • Data Annotations - Provides human-readable descriptions and metadata to guide AI agents in generating accurate queries.
  • Language Model Integrations - Connects external large language model providers to power data analysis and conversational interfaces.
  • Contextual Memory Agents - Stores and retrieves interaction history and user preferences to provide personalized, context-aware responses.
  • Analytical Query Engines - Optimizes performance for complex data requests using pre-aggregations and intelligent caching to serve results from high-speed storage layers.
  • Analytics Dashboards - Promotes internal data visualizations to a published state for broader consumption.
  • Cross-Source Joins - Combines data from different sources using internal storage engines or native database integration tables.
  • Cloud Data Lake Integrations - Integrates with cloud data lakes to enable direct analytical querying.
  • Data Retrieval Interfaces - Fetches analytical results using standard REST, GraphQL, and SQL interfaces.
  • Dimension Definition Reuse - Provides modular dimension definitions to maintain consistent business logic across analytical models.
  • Security Context Compilation - Generates customized data model versions at runtime using security context claims to determine field visibility.
  • External Data Integrations - Provides a unified semantic layer by integrating with diverse data warehouses, databases, and streaming platforms.
  • Model Member Referencing - Allows the creation of complex, interconnected data models by referencing members across cubes.
  • Model-to-Source Routing - Enables multi-database architectures by routing specific data models to their corresponding data sources.
  • Multi-Tenant Data Management - Provisions dedicated compute and storage resources for individual tenants to ensure data residency and performance boundaries.
  • Semantic Metrics - Creates complex measures based on existing data points to perform arithmetic operations and ratios across the semantic layer.
  • Reusable Filter Definition - Enables consistent application of business rules by defining reusable filter segments.
  • SQL Query Interfaces - Provides a database-compatible interface to query semantic models using standard SQL syntax.
  • Analytics Hosting Platforms - Deploys data processing components across models ranging from shared multi-tenant environments to isolated cloud accounts.
  • Private Infrastructure Hosting - Hosts the data plane within a private cloud environment or dedicated subscription to ensure isolated resource management.
  • Self-Hosted Deployment Platforms - Runs a semantic data layer within a containerized environment to manage and serve business intelligence metrics from local infrastructure.
  • API Access Security - Secures analytical endpoints and sensitive data access using token-based authentication for incoming requests.
  • Data Encryption - Secures persisted data-at-rest using cloud provider encryption or modular file encryption.
  • Embedded Session Authentication - Generates temporary session identifiers to facilitate secure, authenticated data access for embedded analytics components.
  • Dynamic Query Contexts - Applies row-level filters and access policies at runtime by injecting authenticated user attributes directly into generated database queries.
  • SCIM Provisioning - Automates user account and group management by syncing identity provider data using the SCIM 2.0 protocol.
  • Policy-Based Access Control - Defines group-scoped policies to control member visibility, row-level filtering, and data masking within the semantic model.
  • Code-First Editors - Provides a browser-based code editor to define and refine semantic data models.
  • Asynchronous Task Queueing - Manages incoming data requests through a distributed task system to deduplicate operations and protect source databases.
  • Embedded Analytics - Embeds interactive dashboards and analytical interfaces directly into custom applications using secure, authenticated sessions.
  • AI Agent Tooling - Enables selection and restriction of external tools to ensure agents remain focused on relevant analytical tasks.
  • AI Agent Workflow Definition - Defines custom instructions, trusted query libraries, and workflows to guide how automated assistants interpret data.
  • Conversation State Persistence - Controls how automated assistants persist memory across sessions to maintain continuity in data-driven conversations.
  • Contextual Customizers - Injects security or tenant-specific information during compilation to tailor dimensions and measures.
  • Model Provider Configurations - Configures external large language model providers for data analysis and agent operations.
  • Analytics Platforms - Semantic layer for AI and embedded analytics applications.
  • Business Intelligence and Analytics - Headless BI platform providing a semantic layer for metrics.
  • Attribute Definitions - Configures dimensions for grouping and filtering data, including specialized time-based attributes.
  • Precomputation Engines - Compiles data models and builds pre-aggregations before a deployment begins serving traffic to ensure optimal query performance.
  • Query Result Caching - Stores pre-computed results and intermediate data layers to minimize latency and reduce load on underlying databases.
  • Materialized Data Summaries - Maintains materialized data summaries in external storage to accelerate query performance and reduce load on primary data sources.
  • Data Model Generation - Automates the creation of metrics and dimensions from existing database tables.
  • Facade Curators - Exposes simplified, logical subsets of the underlying data model to downstream consumers.
  • Unified Batch and Stream Processing Engines - Merges historical warehouse data with real-time streams using pre-aggregations for unified analytical views.
  • Data Querying Interfaces - Delivers semantic data models to external applications and BI tools via standard interfaces.
  • Inheritance Definitions - Reuses measures, dimensions, and joins from parent definitions to reduce code duplication.
  • Model Configuration Editing - Enables flexible development workflows for managing complex data model definitions.
  • Multi-Database Connection Configuration - Allows the platform to connect to and manage multiple distinct data sources simultaneously.
  • Query Optimizations - Implements pre-aggregation strategies and workload-aware settings to reduce total compute costs and improve response times.
  • Reusable Logic Definition - Reduces duplication and ensures consistency by allowing logic to be shared across data model files.
  • Semantic Metadata Retrievers - Fetches the structure of the semantic layer to enable dynamic discovery of data models.
  • SQL Expression Embedding - Allows developers to embed custom SQL logic directly into the semantic model for advanced transformations.
  • SQL Query Execution Engines - Transforms high-level analytical requests into optimized, dialect-specific SQL queries tailored for diverse underlying database engines.
  • Automated Deployment Pipelines - Triggers production builds automatically from repository commits to ensure consistent environment updates.
  • Cloud Infrastructure Provisioners - Provisions and manages analytics components within private cloud environments while maintaining centralized control.
  • Isolation Configurations - Supports single-tenant deployment options and dedicated cloud resources to provide enhanced blast-radius protection for sensitive data environments.
  • Private Infrastructure Management - Provisions and manages platform components within a customer-owned cloud environment to ensure data remains within the private network.
  • Isolated Infrastructure Management - Provisions dedicated single-tenant compute and storage environments within managed or customer-owned cloud accounts to ensure complete resource isolation.
  • Encrypted Connection Handlers - Enforces encryption for all traffic between the analytics layer and data warehouses to protect sensitive information.
  • API Request Authentication - Validates REST and SCIM requests by verifying bearer tokens or API keys in authorization headers.
  • Identity Federation - Uses OIDC-based federated credentials to access cloud services without requiring static secrets or long-lived keys.
  • Identity Provider Role Mapping - Maps external identity provider group memberships to internal platform roles to automate access control.
  • JWT Authentication - Verifies JSON Web Tokens to extract user claims and establish secure contexts for data access rules.
  • OIDC Authentication Plugins - Uses OIDC tokens to assume cloud IAM roles for secure, passwordless access to infrastructure resources.
  • Role-Based Access Controls - Configures granular access policies to include or exclude specific data entities for different user groups.
  • User Access Management - Provides centralized tools for inviting users, assigning roles, and managing account lifecycles.
  • Workload Identity Federation - Issues short-lived, signed tokens to federate access with cloud services, eliminating the need for manual credential rotation.
  • Analytical Data Retrieval APIs - Executes complex data queries via a REST API to return structured results for reporting.
  • Query Performance Monitoring - Tracks execution time, success status, and cache utilization for all data requests to identify bottlenecks.
  • Trusted Query Libraries - Provides a library of pre-approved SQL patterns to guide AI agents toward accurate business logic.
  • User Group Management - Organizes users into collective groups to simplify permission management and apply data access policies.
  • Connection Managers - Manages concurrent database connections to ensure stable performance and prevent resource exhaustion.
  • Approximate Aggregators - Utilizes approximate counting functions within pre-aggregations to improve performance on high-throughput data streams.
  • Data Discovery Tools - Exposes data model structures through a programmatic interface to simplify client-side integration.
  • Caching and Performance - Stores frequently accessed data sets in a temporary layer to reduce response times for incoming analytical requests.
  • Modularization Tools - Organizes complex data definitions into smaller, reusable files to improve maintainability.
  • Trino Connectors - Provides secure connectivity to Trino query engines with custom request routing.
  • Data Model File Generation - Accelerates the setup of the semantic layer by automatically generating model files from database schemas.
  • Data Model Templating - Facilitates dynamic schema generation and code reuse through templating.
  • Abstraction Layers - Creates an abstraction layer over data models to manage governance and control field visibility.
  • Database Connectivity - Provides secure connectivity to Vertica databases for analytical data retrieval.
  • BigQuery Connectors - Supports secure authentication and integration with cloud-based data warehouse services.
  • ClickHouse Connectors - Enables high-performance, real-time analytical connections to database servers.
  • MySQL Connectors - Supports secure, encrypted connections to relational databases with connection pooling.
  • Oracle Connectors - Provides secure connectivity drivers for enterprise database instances.
  • PostgreSQL Connectors - Establishes secure connections to relational database instances for data retrieval.
  • Redshift Connectors - Provides secure connectivity drivers for AWS Redshift data warehouses.
  • Snowflake Connectors - Provides secure connectivity modules for Snowflake cloud data platforms.
  • Database Query Execution - Executes arbitrary database commands against the semantic layer and streams results as newline-delimited JSON.
  • Pre-aggregation Refreshers - Updates and refreshes pre-aggregated datasets in the background to ensure analytical queries remain performant and accurate.
  • Routing Strategies - Routes requests from different tenants to specific database instances while maintaining a unified data model.
  • Entity Relationship Models - Establishes relationships between logical objects to enable multi-table query generation.
  • Programmatic Data Model Generation - Automates model generation and multi-tenant configuration through programmatic scripts.
  • Query Aggregates - Converts aggregate measures into per-row values to enable complex nested calculations.
  • Real-Time Data Streaming - Integrates with real-time streaming databases to enable analytics on live data streams.
  • Distinct Count Optimizers - Leverages database-specific extensions to perform high-performance approximate distinct counting within pre-aggregated data structures.
  • Relationship Modeling - Maps connections between data entities to enable automatic generation of multi-table queries.
  • Reporting Configurations - Provides programmatic interfaces to update configuration and query parameters for analytical reports.
  • Schema Recompilation Triggering - Ensures data models remain up-to-date by automatically recompiling when schema changes are detected.
  • SQLite Integration - Supports querying and managing local SQLite database files as data sources.
  • Pivot Table Aggregators - Combines metrics from multiple fact tables sharing common dimensions without causing row multiplication or data duplication.
  • Time Series Databases - Integrates with high-performance time-series storage engines for temporal data analysis.
  • Visual Data Modeling - Provides an interactive visual interface for designing and managing data model relationships.
  • Cloud Infrastructure Deployment - Provisions and maintains cloud resources including network, compute clusters, and storage within a dedicated customer environment.
  • Deployment Environment Management - Provides separate production, staging, and development environments to manage data model changes and testing workflows independently.
  • Deployment Scaling - Provides tiered hosting options ranging from shared development environments to high-availability production clusters for large-scale multi-tenancy.
  • Platform Resource Management - Provides programmatic access to administrative tasks including deployment management, environment configuration, and user provisioning.
  • Storage Backend Configurations - Directs pre-aggregated data to user-provided object storage to ensure data residency within organizational infrastructure.
  • Storage Connection Management - Configures secure, identity-based access to external cloud storage containers for pre-aggregated data.
  • Branch Synchronizers - Connects version control repositories to staging environments to automatically reflect changes for testing.
  • SQL Server Connectors - Provides secure connectivity drivers for SQL Server database instances.
  • Access Auditing - Visualizes row-level filters, member-level restrictions, and data masking rules grouped by user identity to verify data access configurations.
  • Encryption Key Management - Configures customer-managed keys to encrypt data at rest, supporting key rotation and removal for security compliance.
  • Identity and Role Management - Configures primary IAM roles or service accounts to automatically authenticate internal drivers and operations.
  • Identity Provisioning - Removes users from the system to revoke access and clean up identity records.
  • Identity Synchronization - Automates the synchronization of user identities and group memberships from external providers.
  • OAuth Authentication - Delegates user identity verification to external providers using OAuth tokens while maintaining service account health checks.
  • Role-Based Access Control Systems - Enables the definition of custom access roles with granular privileges for organizational security.
  • SAML Authentication - Integrates with corporate identity providers to allow single sign-on using existing credentials.
  • Secure Connection Handlers - Establishes encrypted communication channels with upstream data sources using custom certificate authorities and client certificates.
  • Health Monitoring Endpoints - Exposes standardized network endpoints that report internal application state and operational health.
  • Branding Customization - Applies custom visual identity elements to embedded analytics surfaces and management consoles.
  • Data Explorers - Provides a web-based interface to select measures and dimensions, execute queries, and visualize results through various chart types.
  • Conversational Analytics Embedding - Integrates chat interfaces directly into custom applications to provide users with interactive, natural language data exploration capabilities.
  • View Composers - Organizes and exposes specific subsets of data members from multiple cubes into unified views.
  • Query Deduplications - Protects data sources by deduplicating redundant analytical queries through managed queues.
  • Recurring Agent Scheduling - Automates the periodic generation of dashboard snapshots and delivers them to configured recipients based on a defined schedule.
  • Agentic Workflow Automations - Packages instructions into named tasks to automate complex data analysis requests within chat interfaces.
  • Conversation Threads - Grants read-only access to chat threads for team members to review previous queries, reasoning, and data visualizations.
  • Automated Reporting - Initiates an immediate update of a stored report to ensure data reflects the most recent information.
  • Workbook Duplicators - Enables the creation of independent workbook copies with automated reference updates.
  • Analytical Databases - Integrates with various analytical database engines to enable semantic modeling and querying.
  • Columnar Storage Engines - Uses columnar storage formats to accelerate analytical query execution.
  • Custom Python Integration - Enables complex model generation logic by integrating custom Python functions.
  • Data Access and Querying - Queries and models data stored in remote object storage files for real-time reporting.
  • Distributed Caches - Deploys a dedicated storage layer to handle in-memory caching and query queues, preventing performance degradation in multi-node environments.
  • Data Persistence and Storage - Persists pre-aggregated query results in cloud storage for durability and consistency.
  • Schema Sandboxes - Creates sandboxed environments for testing schema updates without affecting production analytics.
  • Database Integrations - Integrates streaming database sources for real-time data modeling.
  • Cloud Data Warehouse Connectivity - Provides secure connectivity to enterprise data warehouses for consistent analytics.
  • Elasticsearch Connectors - Integrates search and analytics engines as queryable data sources.
  • Distributed SQL Databases - Integrates with distributed SQL systems to support high-throughput analytical querying.
  • Dynamic Data Model Execution - Supports flexible, runtime-generated analytics configurations by loading models dynamically.
  • Dynamic Data Model Generation - Supports dynamic model creation by fetching and registering definitions at runtime.
  • Resource Scaling Strategies - Distributes query execution across multiple worker nodes to handle high concurrency and large data volumes.
  • Model Reference Transpilation - Optimizes and resolves data model references to ensure consistent query performance.
  • Tenant Request Routing - Directs traffic from specific users to dedicated deployment nodes to optimize cache utilization and ensure consistent performance.
  • Query Caching Strategies - Adjusts cache behavior for individual requests to balance the need for real-time data freshness against faster query performance.
  • Reporting Tools - Programmatically creates and organizes data reports to track metrics and visualize query results.
  • Time-Series Data Modeling - Groups data by time granularities to enable temporal trends and patterns in reporting.
  • Dashboard Management - Supports modifying dashboard configurations, layouts, and themes within analytical workbooks.
  • Workspace Organization - Supports hierarchical folder structures for grouping and managing analytical assets.
  • Container Orchestration Deployments - Orchestrates multi-node deployments using container configurations to manage API instances and background workers for high availability.
  • Deployment Configuration - Manages environment variables and update channels to control runtime behavior of hosted instances.
  • Pre-aggregation Capacity Managers - Configures dedicated storage workers to build and persist data summaries, improving query speed and expanding available storage for large datasets.
  • Data Masking - Replaces restricted data with transformed values to prevent exposure of sensitive information.
  • Attribute Synchronizations - Automates the synchronization of user profile information from external identity providers to ensure consistent metadata.
  • User Role Management - Supports the creation of specialized user roles with granular permissions tailored to organizational needs.
  • Resource Access Policies - Configures granular IAM trust policies to manage access to cloud data services.
  • Identity Lifecycle Management - Automates user lifecycle management including onboarding and offboarding through identity synchronization.
  • Private Network Security - Routes API traffic through private network tunnels to ensure data remains within internal infrastructure.
  • Synchronization Tokens - Creates temporary authentication credentials to enable secure metadata synchronization and programmatic management of cloud-hosted resources.
  • Embed Token Issuance - Exchanges single-use session identifiers for signed tokens to authenticate analytics components within browser environments.
  • Runtime Variable Injections - Utilizes runtime variables within model definitions to dynamically generate queries.
  • Large Dataset Optimizations - Optimizes performance for massive data volumes by offloading large pre-aggregation datasets to external object storage.
  • Workbook Management APIs - Creates and organizes analytical workbooks to structure dashboards, reports, and metadata for consistent data exploration.
  • API Performance Monitoring - Adjusts the number of active API instances to handle varying query volumes and ensures consistent performance under high traffic loads.
  • Security Audit Logs - Tracks and retrieves execution history of security policies and automated actions.
  • Usage Monitoring - Tracks and optimizes infrastructure usage by automatically scaling or suspending resources based on workload activity.
  • Test Isolation - Creates branch-based development environments that hot-reload data model changes for testing.
  • Custom Color Palette Definitions - Defines branded color schemes for consistent visual styling across charts and dashboards.
  • Dashboard Styling Systems - Controls the appearance of dashboard elements through reusable visual themes.
  • Request Routing - Directs incoming analytical requests to either raw data sources or pre-aggregated storage for performance.

    • Historial de estrellas

    Gráfico del historial de estrellas de cube-js/cubeGráfico del historial de estrellas de cube-js/cube

    Preguntas frecuentes

    ¿Qué hace cube-js/cube?

    Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools.

    ¿Cuáles son las características principales de cube-js/cube?

    Las características principales de cube-js/cube son: AI Query Orchestrators, Natural Language Querying Interfaces, Business Intelligence Platforms, Data Modeling Frameworks, Metric and Dimension Configuration, Semantic Data Models, Embedded Analytics Solutions, AI-Powered Data Assistants.

    ¿Qué alternativas de código abierto existen para cube-js/cube?

    Las alternativas de código abierto para cube-js/cube incluyen: dbt-labs/dbt-core — dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control.… apache/superset — Superset is a web-based business intelligence platform designed for data exploration, visualization, and interactive… aws/aws-cdk — The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision… apache/incubator-superset — This project is a business intelligence suite and SQL data visualization platform used for data analysis, reporting,… twentyhq/twenty — Twenty is a headless customer relationship management framework that enables developers to build, version, and deploy… metabase/metabase — Metabase is a business intelligence platform designed to connect to various storage systems and relational databases…

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