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Rill Documentation

website·Indexed 34 days ago

https://docs.rilldata.com

Documentation for Rill, featuring Rill Developer and Rill Cloud. Covers project configuration, OLAP connectors, dashboard deployment via CLI and GitHub, environment management, and AI configuration for AI Chat and MCP.

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What's inside Rill

  1. Overview of Rill Cloud features for end-users

    Rill Cloud is a managed analytics platform for business analysts and executives to explore data without SQL. Key features include:

    • AI Chat: Natural language querying for instant insights.
    • Interactive Dashboards: Real-time slicing, dicing, and drilling into metrics.
    • Pivot Tables: Drag-and-drop Excel-style cross-tabulations.
    • Canvas Dashboards: Custom layouts with multiple visualizations.
    • Bookmarks: Saved dashboard states (filters, metrics, dimensions) for quick access or sharing.
    • Public URLs: External sharing of dashboards without requiring Rill accounts.
    • Reports & Alerts: Automated email/Slack reports and condition-based notifications.
    • Administration: Management of organization settings, billing, and user access controls.

  2. Overview of Rill Developer for data modeling

    Rill Developer provides an IDE-like interface for developers to manage the data pipeline from import to visualization. The workflow involves importing data into the OLAP engine, performing last-mile ETL using SQL models organized as a Directed Acyclic Graph (DAG), and materializing a "One Big Table" (OBT) to support dashboarding. Project specifications for dimensions and measures are defined and tested within the Developer's dashboard preview. The underlying configuration and logic are stored primarily as SQL and YAML files.

  3. Overview of Rill's core capabilities

    Rill is a BI-as-Code platform designed for high-performance analytics on large datasets. Key features include:

    • Sub-second Query Performance: Optimized aggregation and pruning allow for instant interactions even with hundreds of millions of rows.
    • BI-as-Code: The analytics stack is managed via SQL and YAML, enabling the use of version control, CI/CD, and collaborative workflows.
    • Direct Data Lake Integration: Connects directly to cloud data warehouses (BigQuery, Snowflake), lakehouses, or object storage (S3, GCS, Azure), reducing the need for complex ETL processes.
    • Integrated OLAP Engine: Features a built-in, in-memory OLAP engine that automatically handles data orchestration and query execution.

  4. Overview of Rill's core capabilities

    Rill provides a developer-friendly workflow to bring fast, interactive analytics directly to data lakes or warehouses, solving the performance gap typically found in fragmented data stacks. Key capabilities include:

    • Data Modeling: Using SQL to transform and prepare data.
    • Metrics Layer: Defining business KPIs and calculations.
    • Deployment: Sharing dashboards with teams.

  5. Overview of Rill Cloud Dashboards

    Rill Cloud provides interactive dashboards designed for exploring, analyzing, and sharing data. These dashboards enable users to slice, dice, and drill down into metrics to discover business insights.

  6. Overview of Rill MCP Server

    The Rill MCP Server uses the Model Context Protocol to connect Rill projects to external AI assistants (such as Claude Desktop). It exposes Rill's essential APIs to LLMs, allowing users to query Rill data and metrics views through AI assistants while maintaining security and access controls. The server is specifically designed for data analysts to consume existing metrics views rather than for data engineers to create them.

  7. Overview of Rill Developer capabilities

    Rill Developer is a local application used to build end-to-end analytics pipelines. It allows developers to perform the following tasks from a local machine:

    • Connect to various data sources (e.g., S3, GCS, BigQuery, Snowflake, ClickHouse).
    • Perform last-mile transformations using SQL or YAML models.
    • Define a metrics layer consisting of measures and dimensions.
    • Preview and build interactive dashboards (Explore and Canvas types).
    • Create custom API endpoints to retrieve data from Rill externally.

  8. Overview of Rill Visual Analytics

    Rill provides business-speed visual analytics by utilizing OLAP engines to create quick, reactive dashboards. It supports two primary types of visualization: reactive dashboards for real-time drilling and decision-making, and traditional 'Canvas Dashboards'.

  9. Overview of Rill Admin API capabilities

    The Rill Admin API allows for the programmatic management of Rill organizations. Key capabilities include:

    • Organization Management: Adding/removing users and managing roles/permissions.
    • Project Management: Creating, updating, and deleting projects.
    • User Management: Provisioning users, managing attributes, and issuing tokens.
    • Service Management: Creating and managing service accounts.
    • Deployment Management: Starting, stopping, and configuring deployments.
    • Token Management: Issuing ephemeral user tokens with custom attributes.

  10. Overview of Rill Developer capabilities

    Rill Developer is a toolkit for the end-to-end analytics workflow, enabling users to transform raw data into actionable insights. Its core capabilities include:

    • Data Connectivity: Connecting to databases, cloud storage, and APIs.
    • Data Transformation: Built-in ETL tools for cleaning and preparing data.
    • Metrics Layer: Defining business KPIs and calculations.
    • Visualization: Creating interactive, AI-powered dashboards.
    • Deployment: Publishing and sharing analytics with teams in production.

  11. Overview of Rill project YAML file types

    Rill projects utilize various YAML file types to define different components of the data stack, including: Connector, Source, Models, Metrics View, Canvas Dashboard, Explore Dashboard, Alert, Report, API, Theme, Component, and Project configurations.

  12. Understand and create Metrics Views in Rill

    A metrics view is a semantic layer where you define measures (numeric aggregates) and dimensions (attributes for slicing and filtering) based on a raw data model. This layer transforms raw data into a format usable by Explore and Canvas dashboards. Metrics views can be created using an AI generator, a visual editor, or by manually editing YAML files.