These are the main options for a clickhouse integration dbt setup:

1. dbt → ClickHouse® via dbt-clickhouse community adapter (self-managed or ClickHouse® Cloud)
2. dbt → ClickHouse® Cloud via the official dbt Cloud + ClickHouse® Cloud connection
3. dbt → Tinybird as the ClickHouse®-powered transformation and serving layer

dbt (data build tool) is the most widely adopted SQL-based transformation framework for data teams. ClickHouse® is a columnar OLAP database that handles billions of rows in sub-second queries. Connecting the two lets data engineers run dbt run against ClickHouse® tables, materialise models as ClickHouse® views and tables, and serve fast analytical queries downstream.

A clickhouse integration dbt pipeline lets teams use familiar dbt concepts — models, refs, tests, seeds — while gaining ClickHouse®'s columnar performance for the transformation layer. This is especially valuable for teams whose dbt models run against BigQuery or Snowflake today but need sub-second query performance on large datasets at lower cost.

Before you pick a path, consider these questions:

• Do you need full dbt feature parity (tests, docs, snapshots, seeds) or primarily the model + materialisation workflow?
• Is your ClickHouse® self-managed, or are you using ClickHouse® Cloud?
• Do you also need to expose transformed data as REST APIs for applications, or is the output consumed only by BI tools?

Three ways to implement clickhouse integration dbt

This section covers the three main integration paths, with configuration and model code for each.

Option 1: dbt → ClickHouse® — dbt-clickhouse community adapter

The most direct approach. The dbt-clickhouse community adapter translates dbt's materialisation patterns (view, table, incremental) into ClickHouse®-native DDL and DML. Install it alongside dbt-core, configure a ClickHouse® profile, and run dbt run against your ClickHouse® instance.

How it works: the adapter connects to ClickHouse® via the HTTP interface (port 8443 for TLS) using clickhouse-connect. Each dbt model compiles to a ClickHouse® CREATE VIEW, CREATE TABLE AS SELECT, or INSERT INTO … SELECT depending on materialisation. ClickHouse®-specific materialisation settings are passed via config() blocks.

Installation:

pip install dbt-clickhouse

profiles.yml configuration:

clickhouse_project:
  target: dev
  outputs:
    dev:
      type: clickhouse
      schema: analytics
      host: your-clickhouse-host
      port: 8443
      user: dbt_user
      password: "{{ env_var('CLICKHOUSE_PASSWORD') }}"
      secure: true
      verify: true
      connect_timeout: 10
      send_receive_timeout: 300
      sync_request_timeout: 5
      compress_block_size: 1048576
      compression: ""
      database: analytics
      database_engine: Atomic
      cluster: ""

dbt model — incremental materialisation with ClickHouse®-specific settings:

-- models/events_daily.sql
{{
  config(
    materialized='incremental',
    engine='ReplacingMergeTree(updated_at)',
    order_by='(event_date, event_type, country)',
    partition_by='toYYYYMM(event_date)',
    unique_key='(event_date, event_type, country)',
    incremental_strategy='delete+insert'
  )
}}

SELECT
    toDate(event_time)          AS event_date,
    event_type,
    country,
    count()                     AS total_events,
    uniq(user_id)               AS unique_users,
    sum(revenue)                AS total_revenue,
    now()                       AS updated_at
FROM {{ source('raw', 'events') }}
WHERE event_time >= today() - INTERVAL 1 DAY