Features
dbt utils is a community-maintained dbt package that adds time-saving utility macros for analytics engineers, such as generating surrogate keys, safely unioning models, and automating date spines.
dbt utils is an open-source dbt package that ships plug-and-play macros, letting teams write less Jinja and ship data models faster while preserving testing rigor.
Utility macros abstract boilerplate logic—surrogate keys, unions, date spines—so engineers spend time on business logic, not repetitive SQL. Fewer lines mean fewer bugs and easier code review.
Add "dbt_utils" with a compatible version to your packages.yml file, then run dbt deps. The package is versioned alongside dbt Core releases for smooth upgrades.
packages:
- package: dbt-labs/dbt_utils
version: ">=1.0.0"
generate_surrogate_key hashes multiple columns into one primary key. union_relations dynamically stacks tables with differing schemas. date_spine creates calendar tables. Each macro is battle-tested by the dbt community.
{{ dbt_utils.generate_surrogate_key(['id','updated_at']) }}
{{ dbt_utils.union_relations(relations=[ref('events_2023'), ref('events_2024')]) }}
Macros like expect_equal_rowcount and recency extend dbt tests, catching data freshness and duplication issues early in CI pipelines.
Yes. Galaxy’s IDE detects dbt projects, so you can author macros and run dbt run locally. The AI copilot autocompletes dbt_utils macro names and arguments, accelerating model development.
Pin package versions, document macro calls in code comments, and prefer dbt_utils macros over hand-rolled Jinja to standardize style across teams.
Combine date_spine with generate_surrogate_key to create a grain-correct metrics table keyed on date and dimension values.
with spine as (
{{ dbt_utils.date_spine(
datepart='day',
start_date="2023-01-01",
end_date="2024-12-31") }}
),
metrics as (
select *,
{{ dbt_utils.generate_surrogate_key(['date_day','dim_id']) }} as sk
from spine left join {{ ref('dim_table') }} using (date_day)
)
select * from metrics
Missing package versions, macro namespace typos, or unsupported warehouse functions cause most issues. Check manifest.json or run dbt --debug to locate the problem quickly.
Analytic codebases grow quickly. Reusing proven macros like those in dbt utils reduces maintenance, enforces consistency, and shortens onboarding time for new engineers. Standardized logic also improves test coverage and makes CI pipelines more reliable.
Yes. While community-maintained, the package lives under the dbt-labs GitHub organization and follows dbt’s release cadence.
Absolutely. Most macros are warehouse-agnostic; exceptions are noted in the docs.
Galaxy autocompletes macro signatures, highlights Jinja errors inline, and lets you run dbt commands without leaving the IDE.
Yes. Thousands of companies run dbt utils in production; just pin versions and add tests.
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