---
title: "Rill and ClickHouse: real-time operational BI for a metered world"
description: "Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments."
---

# Rill and ClickHouse: real-time operational BI for a metered world

[Clickhouse](https://yomu.fyi/company/clickhouse) · ClickHouse · Apr 1, 2026

**Type:** Problem & solution

## Summary

Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments.

## Context

Modern organizations generate vast streams of granular operational data from cloud infrastructure, payment platforms, and AI token usage across disparate systems like AWS, Google Cloud, and Stripe. However, isolated vendor dashboards fail to present a cohesive picture of business health, while traditional BI systems rely on caching and cannot efficiently aggregate massive event volumes in real time.

## Approach / What changed

Rill integrates declarative ingestion using the dlt Python framework, real-time aggregation via ClickHouse, and a metrics-first operational BI layer defined in SQL and YAML. Developers configure sources, metrics, and security policies as code, running local browser instances against ClickHouse partitions to test and version-control analytics before deploying them alongside AI conversational interfaces.

## Takeaways

- Rill employs a BI-as-code model where sources, transformations, and metrics are declared in SQL and YAML, enabling automated dashboard generation and Git-based versioning.
- Conversational AI agents achieve higher reliability when operating over a defined semantic layer rather than performing direct text-to-SQL across hundreds of raw tables.
- High-speed aggregation in ClickHouse removes the need for query caching layers, allowing fast slicing, dicing, and verifiable traceability for live event streams.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Developer Experience](https://yomu.fyi/topic/developer-experience), [LLMs](https://yomu.fyi/topic/llm), [Performance](https://yomu.fyi/topic/performance)

- Source: [Clickhouse](https://clickhouse.com/blog/rill)
- Source URL: https://clickhouse.com/blog/rill
- Ingested by Yomu: 2026-08-28T01:23:38.687Z

[Read original post](https://clickhouse.com/blog/rill)