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description=Data teams often struggle with a fragmented stack: while data lakes and warehouses are cost-effective for large volumes, they often lack the sub-second performance required for interactive, operational analytics. Engineering teams spend too much time managing ETL pipelines and answering ad-hoc requests, while business users are stuck with slow, static dashboards that don t allow for true exploration.;

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why rill rill skip to main content developers guide reference project files time syntax rill iso 8601 url parameters cli rest api contact us blog github search get started install rill developer agentic quickstart quickstart why rill build your first project connectors models metrics views dashboards custom apis alerts project configuration ai configuration organize your code files external ide integration debugging in rill developer deploy rill cloud vs rill developer configure deployment credentials deploy dashboards managing project errors embed iframe postmessage api tutorials other release notes home get started why rill on this page why rill data teams often struggle with a fragmented stack while data lakes and warehouses are cost effective for large volumes they often lack the sub second performance required for interactive operational analytics engineering teams spend too much time managing etl pipelines and answering ad hoc requests while business users are stuck with slow static dashboards that don t allow for true exploration rill bridges this gap by bringing fast interactive analytics directly to your data lake or warehouse with a developer friendly workflow what makes rill different fast interactive dashboards on large datasets rill powers sub second queries and instant interactions even on hundreds of millions of rows by optimizing aggregation and pruning bi as code manage your entire analytics stack with code sql and yaml bringing version control ci cd and collaboration to your dashboards works directly with your data lake connects directly to your cloud data warehouse bigquery snowflake lakehouse or object storage s3 gcs azure without complex etl built in olap an integrated in memory olap engine handles data orchestration and query execution automatically for blazing speed how it works rill automatically turns your sql data models into interactive exploratory dashboards it ingests data from your external sources into an embedded high performance olap engine by defining metrics and dimensions upfront rill aggregates and optimizes the data creating a responsive interface that allows users to slice and dice billions of records instantly without waiting for query processing this approach means engineering teams can orchestrate data out of cloud data warehouses or object stores into the fast serving layer define metrics dimensions in metrics view to automatically aggregate and prune raw tables deploy your project to rill cloud without managing separate bi servers or database infrastructure built for operational analytics operational intelligence provides decision making capabilities that are complementary to traditional business intelligence bi business intelligence drives complex decisions that occur daily or weekly often on historical data e g how did we perform last quarter operational intelligence fuels fast frequent decisions on real time and near time data by hands on operators e g why is site latency spiking right now or which campaign is driving traffic this hour rill is built for this fast mode it empowers product managers operations teams and engineers to explore data at the speed of thought identifying trends and anomalies as they happen without waiting for pre computed reports ai native analytics rill s code first architecture yaml and sql files that define your entire analytics stack makes it uniquely suited for ai powered workflows build with ai agents because rill projects are just files ai coding agents like claude code and cursor can build and iterate on your entire project connect data sources write transformation models define metrics views and create dashboards run rill init to generate agent instructions and an mcp connection out of the box see the agentic quickstart to get started chat with your data ai chat is built into rill cloud letting you ask questions about your data in natural language responses are grounded in your predefined measures and dimensions so answers are accurate and governed not hallucinated from raw tables every response includes links back to your explore dashboards so you can verify the numbers connect external ai tools the rill mcp server connects your projects to claude desktop chatgpt and other mcp compatible ai assistants your team gets the same governed analytics in whatever ai tool they prefer with security and access controls enforced by rill improve ai with instructions add ai_instructions to your project and metrics view yaml files to give ai agents context about your business logic terminology and data quirks improving the quality of responses across all ai surfaces key benefits performance rill achieves performance that end users love by serving dashboards from in memory data models often reducing the data footprint by 10 100x compared to raw events developer experience define your entire analytics stack from data models to dashboards using code this brings the benefits of version control collaboration and automation that software development teams rely on cost effective build analytics directly on your storage rill lowers costs because data can be transformed in place without duplication in a heavy enterprise warehouse ai native build projects with ai coding agents chat with your data in natural language and connect external ai tools all governed by your predefined metrics previous quickstart next build what makes rill different how it works built for operational analytics ai native analytics build with ai agents chat with your data connect external ai tools improve ai with instructions key benefits 2026 rill data inc privacy policy terms of service community policy contributing