How to Integrate ClickHouse with dbt in PostgreSQL

Why pair dbt with ClickHouse?

dbt brings version-controlled, testable transformations to ClickHouse’s lightning-fast analytics engine. Together they let analysts deploy reproducible pipelines without writing orchestration code.

Which drivers and adapters are required?

Install the official dbt-adapter: pip install dbt-clickhouse. The package ships with a SQLAlchemy driver so no separate ODBC layer is needed.

How do I write profiles.yml for ClickHouse?

Create a profile keyed by project name. Supply host, port, database, and optional user credentials. dbt will open HTTP connections on port 8123 by default.

Example profiles.yml entry

clickhouse_project:
target: dev
outputs:
dev:
type: clickhouse
host: localhost
port: 8123
database: default
schema: analytics
user: default
password: ''
threads: 4

How do I model ecommerce data?

Place a file like models/orders.sql and reference the source tables.

Sample model

-- models/orders.sql
select o.id as order_id,
c.name as customer_name,
o.order_date,
o.total_amount,
sum(oi.quantity) as items_count
from {{ source('raw', 'Orders') }} o
join {{ source('raw', 'Customers') }} c on c.id=o.customer_id
join {{ source('raw', 'OrderItems') }} oi on oi.order_id=o.id
group by all

Which materializations work best?

Use table when you want dbt to CREATE TABLE … ENGINE = MergeTree. Choose view for lightweight virtualization or incremental when daily partitions land in ClickHouse.

How do I run and test?

dbt run compiles models and pushes SQL to ClickHouse. dbt test validates assumptions like unique primary keys in Customers.

What are best practices?

Partition large tables (e.g., on order_date) to keep merges quick. Use materialized views for roll-ups. Commit generated DDL to git for reproducibility.

Common mistakes and fixes

Using view for huge aggregations. Views render at query time and can exhaust memory. Switch to table materialization.

Ignoring data types in models. ClickHouse infers types; unexpected Nullable columns appear. Cast explicitly inside SELECT or use dbt config(column_types=...).

Can I schedule runs?

dbt Cloud, Airflow, or GitHub Actions invoke dbt run on every commit, rebuilding ClickHouse tables automatically.

Next steps

Add tests, enable incremental materializations, and monitor query logs to fine-tune engines and partitions.