dbt

dbt helps data teams model and transform analytical data. Use the Altertable adapter to run dbt models directly in your Altertable lakehouse. You can also connect a dbt repository to make project definitions, lineage, and semantic metadata available to Altertable and its agents.

Installation

For a worked example of organizing raw data, transformations, and marts, follow the medallion architecture guide.

The Altertable adapter for dbt is available on PyPI and currently requires Python 3.10+ with dbt-core >=1.8,<2.0.

pip install dbt-altertable

If you manage Python dependencies with uv, you can install it with:

uv add dbt-altertable

Configuration

Add an Altertable profile to your profiles.yml file. By default, dbt looks for profiles in ~/.dbt/profiles.yml, but you can also place it in your project directory or specify a custom location using the --profiles-dir flag. Learn more about dbt profiles in the dbt documentation.

Profile Example

your_project_name:
target: dev
outputs:
  dev:
    type: altertable
    username: YOUR_LAKEHOUSE_USERNAME
    password: YOUR_LAKEHOUSE_PASSWORD
    database: your_catalog_name
    schema: your_schema_name
    compute_size: XL

Configuration Parameters

• type: Must be set to altertable (required)
• username: Your lakehouse username (required) — find this in the Credentials section of the dashboard
• password: Your lakehouse password (required) — find this in the Credentials section of the dashboard
• database: The name of your Altertable catalog where models will be materialized (required)
• schema: The schema within your catalog where models will be created (required)
• compute_size: Optional compute size for your workload. Omit it to use the server default.

Usage

Once configured, you can run dbt commands against Altertable:

# Test your connection
dbt debug

# Run your models
dbt run

# Test your models
dbt test

# Generate and serve documentation
dbt docs generate
dbt docs serve

Learn more about dbt commands in the dbt documentation.

Sync project knowledge

Connect your dbt project as a knowledge repository. Altertable detects dbt_project.yml at the repository root or in a subdirectory and turns project source files into searchable knowledge entries. A repository can contain multiple dbt projects.

In Knowledge, select New knowledge, choose Repository, and connect your GitHub or GitLab account. Choose the repository and branch, then select Add repository. Leave the branch empty to use the repository's default branch.

Which files need to be committed?

For Git-based sync, commit your dbt project source files to the connected branch. Include the artifacts below for lineage, model enrichment, and generated metric definitions. Contact us if you need to sync artifacts from a bucket.

Repository sync does not run dbt to generate artifacts. Without a valid manifest.json, Altertable still extracts knowledge from the source files, but skips manifest-based lineage and semantic model enrichment.

Generate and refresh the artifacts

From the directory containing dbt_project.yml, install project dependencies and parse the project. Use the target whose catalog and schema names match the data you want to describe in Altertable. Replace prod with that target's name in your dbt profile:

dbt deps
dbt parse --target prod
git add -f target/manifest.json target/semantic_manifest.json
git commit -m "docs(dbt): refresh project artifacts"
git push

Commit both artifacts under target/ relative to dbt_project.yml.

After pushing, select Sync now from the repository menu. Regenerate and commit the artifacts when your dbt project changes.

Enrich existing semantic models

With a valid manifest.json, Altertable can update existing semantic models with dbt descriptions, column metadata, supported measures and dimensions, and relationships derived from entities. It matches each dbt model to an existing Altertable model by catalog, schema, and table name, using the dbt alias when configured. This enrichment does not materialize tables or create missing semantic models.

Metric definitions remain available as knowledge even when this enrichment cannot import them as native measures. For example, filtered simple metrics are not imported as unfiltered Altertable measures.

Run dbt in a task

A dbt task runs a project from a connected repository in an isolated environment and writes its models to an Altertable catalog. Altertable supplies the dbt profile, so the task needs no profiles.yml or credentials. Creating one requires permission to manage catalogs.

Open Tasks → New task and choose dbt task under Set up manually:

1. Under Project, select the Repository. Set Project directory when dbt_project.yml is not at the repository root, and optionally a Git ref.
2. Under Command, choose build, run, or source freshness, and enter a Selector such as tag:daily.
3. Under Destination, choose the Catalog and Schema to write models to.
4. Set a Schedule, then select Create dbt run.

SQL Dialect

Altertable uses DuckDB as its query engine, so write models with DuckDB-compatible functions, syntax, and data types. The adapter builds on dbt-duckdb and automatically dispatches duckdb__ macros from your project and installed packages.

Learn more about DuckDB SQL in the DuckDB documentation.

MetricFlow

The adapter can run dbt Semantic Layer and MetricFlow workloads by rendering MetricFlow SQL with DuckDB-compatible syntax. Keep your semantic models in dbt as usual, then run MetricFlow against the same Altertable profile.

This works best when your metrics use DuckDB-compatible functions and data types. If a metric relies on warehouse-specific SQL, adapt the expression to DuckDB SQL before running it on Altertable.

Seeds

By default, dbt seed inserts rows in batches so seeds work reliably across local files and remote execution. When you want a direct DuckDB COPY path for a local seed file, set the seed config fast: true.

seeds:
  your_project_name:
    +fast: true

Use the default batched path unless you know the seed file is accessible from the process running dbt.

Incremental models

The adapter supports delete+insert, merge, and sequential microbatch incremental strategies. With delete+insert, configure a stable unique_key so each run deletes matching destination rows before inserting the updated batch:

{{ config(
    materialized='incremental',
    incremental_strategy='delete+insert',
    unique_key='id'
) }}

SELECT *
FROM {{ source('app', 'orders') }}
{% if is_incremental() %}
WHERE updated_at > (SELECT max(updated_at) FROM {{ this }})
{% endif %}

Each dbt connection uses an isolated Flight session, so concurrent model work does not share temporary session state.

For more information about incremental strategies and adapter configuration, see the dbt documentation and the Altertable dbt adapter.

Persisting descriptions

The adapter supports dbt's persist_docs setting for model and column descriptions.

Enable it in dbt_project.yml or on a model:

models:
  your_project_name:
    +persist_docs:
      relation: true
      columns: true

After dbt run, dbt writes model and column descriptions to Altertable with DuckDB COMMENT ON TABLE and COMMENT ON COLUMN statements. View column comments are skipped gracefully when DuckDB cannot apply them.

Source Code

The adapter source code is available at github.com/altertable-ai/dbt-altertable