dbt-vertica
Official vertica adapter plugin for dbt (data build tool)
Decision gist · record as of 2026-08-14
Yes, if you use Vertica and dbt. The adapter is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and supports most dbt core features needed for typical data transformation workflows. Install friction is low. The only caveat is that external tables are untested and snapshot check_cols are not supported—verify these gaps don't block your use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Vertica database instance (version 23.4.0-0 or compatible) and valid connection credentials in profiles.yml.
- Low install friction; ships as a wheel with five runtime dependencies including dbt-core and vertica-python.
- Marked active maintenance with a release 38 days ago.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 (permissive) — you can use, modify, and distribute this package freely in commercial and private projects, with minimal restrictions.
last release 2026-07-07 (38 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 799,853 downloads/mo, #5,025 on PyPI
Alternatives
Verify before relying
pip install dbt-vertica
Then configure profiles.yml with:
type: vertica
host: [hostname]
port: 5433
username: [user]
password: [pass]
database: [db]
schema: [schema]
Run: dbt run- Whether external tables are fully supported (fact sheet marks as 'Untested').
- Production readiness of snapshot check_cols feature (marked 'No' in support matrix).
- Compatibility with Vertica versions outside the tested range (23.4.0-0).
What it is and what it does
dbt-vertica is a dbt adapter that bridges dbt (a data transformation tool) and Vertica databases. It uses vertica-python to handle the connection layer, allowing you to write dbt models, tests, and documentation that execute as SQL transformations directly in Vertica. The adapter supports most core dbt features including table, view, and incremental materializations with multiple merge strategies, snapshots, seeds, tests, and unit testing.
You install it once alongside dbt-core, configure a profiles.yml file with your Vertica connection details (host, port, credentials, database, schema), and then use standard dbt commands (dbt run, dbt test, dbt build) to orchestrate your data pipeline. The adapter handles the translation of dbt's transformation logic into Vertica-native SQL, managing incremental updates, transaction control, and multi-threaded execution.
Use it for
- Build incremental fact tables in Vertica using dbt's merge or delete+insert strategies to avoid full table rewrites.
- Run dbt tests and documentation generation on Vertica models as part of a data quality and governance workflow.
- Orchestrate multi-step SQL transformations in Vertica using dbt's DAG and dependency resolution.
- Execute dbt unit tests directly against Vertica to validate transformation logic before production deployment.
- Manage Vertica schema evolution and snapshots using dbt's snapshot feature for slowly-changing dimensions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Vertica and dbt.
The adapter is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and supports most dbt core features needed for typical data transformation workflows. Install friction is low. The only caveat is that external tables are untested and snapshot check_cols are not supported—verify these gaps don't block your use case.
Install
dbt-vertica on PyPI
Before you install
Low install friction; ships as a wheel with five runtime dependencies including dbt-core and vertica-python. Marked active maintenance with a release 38 days ago.
Requires a running Vertica database instance (version 23.4.0-0 or compatible) and valid connection credentials in profiles.yml.
License in practice
Apache License 2.0 (permissive) — you can use, modify, and distribute this package freely in commercial and private projects, with minimal restrictions.
Quickstart
pip install dbt-vertica
Then configure profiles.yml with:
type: vertica
host: [hostname]
port: 5433
username: [user]
password: [pass]
database: [db]
schema: [schema]
Run: dbt runVerify before relying
- Whether external tables are fully supported (fact sheet marks as 'Untested').
- Production readiness of snapshot check_cols feature (marked 'No' in support matrix).
- Compatibility with Vertica versions outside the tested range (23.4.0-0).
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.8.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesdbt-corevertica-pythondbt-tests-adapterpython-dotenvpytest |
| Maintenance | Actively maintained 38 days since the last release |
| First released | |
| Downloads | 799,853 / month, #5,025 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: DatabaseTopic :: Database :: Database Engines/ServersTopic :: Database :: Front-EndsTopic :: Software Development :: Libraries :: Python Modules |
Evidence: dbt_vertica-1.8.6-py3-none-any.whl
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See also dbt-clickhouse · dbt-trino · vertica-python · dbt-fabric · dbt-sqlserver · dbt-dremio · dbt-athena-community · dbt-postgres · dbt-snowflake · sqlalchemy-vertica