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What is Dozer?

Dozer is a real-time analytical platform that turns data sources into queryable APIs and dashboards. Built in Rust with ClickHouse as its underlying data engine, it handles both batch and streaming data through connectors, SQL transformations, and a semantic layer defined in YAML. You write standard SQL to transform your data, and Dozer automatically exposes it via REST APIs with generated documentation. It's designed for teams building customer-facing analytics products or internal decision dashboards without needing extensive data engineering infrastructure. The platform includes native support for change data capture, LLM integration for building RAG applications, and role-based access control, making it suitable for startups through to enterprises.

Key Features

Real-time connectors

ingest data from applications and APIs with CDC support

SQL transformations

define data pipelines and materialized views in standard SQL

Automatic API generation

expose queries as REST endpoints with OpenAPI documentation

LLM and RAG support

integrate with language models and build chatbot experiences

YAML-based semantic layer

manage calculations, security rules, and metadata in version control

ClickHouse backend

use columnar storage for fast analytical queries at scale

Batch and streaming

combine historical data with real-time updates in the same queries

Pros & Cons

Advantages

  • Fast query performance due to Rust implementation and ClickHouse architecture
  • SQL-first approach means existing data analysts can contribute without learning new languages
  • API-first design eliminates the need for separate backend code to expose data
  • Git-friendly configuration makes it easy to review and version control data pipelines
  • Low operational overhead compared to building a custom data stack

Limitations

  • Requires some SQL and YAML knowledge; not truly no-code for complex transformations
  • Pricing details not publicly listed, may need to contact sales for enterprise plans
  • Relatively new platform with smaller ecosystem compared to established analytics tools
  • Limited documentation on advanced use cases and edge cases

Use Cases

Building customer-facing dashboards and analytics products with real-time data

Creating internal decision support systems that update instantly as data changes

Exposing operational data via APIs for third-party integrations

Building RAG applications that search and reason over your proprietary data

Replacing custom ETL infrastructure for small to mid-sized teams