cube-js/cube

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.
• 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.

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