Dagster orchestrates dbt alongside other technologies, so you can combine dbt with Spark, Python, etc. in a single workflow. Dagster’s software-defined asset abstractions make it simple to define data assets that depend on specific dbt models, or to define the computation required to compute the sources that your dbt models depend on.
Related documentation pages: dbt and dbt Cloud.
Commands for using a dbt project in Dagster.
dagster-dbt project [OPTIONS] COMMAND [ARGS]...
This command will invoke prepare_and_package on DbtProject found in the target module or file.
Note that this command runs dbt deps and dbt parse.
dagster-dbt project prepare-and-package [OPTIONS]
Options
Required The file containing DbtProject definitions to prepare.
This command will initialize a new Dagster project and create directories and files that load assets from an existing dbt project.
dagster-dbt project scaffold [OPTIONS]
Options
Required The name of the Dagster project to initialize for your dbt project.
The path of your dbt project directory. This path must contain a dbt_project.yml file. By default, this command will assume that the current working directory contains a dbt project, but you can set a different directory by setting this option.
Here, we provide interfaces to manage dbt projects invoked by the local dbt command line interface (dbt CLI).
Create a definition for how to compute a set of dbt resources, described by a manifest.json.
When invoking dbt commands using DbtCliResource’s
cli() method, Dagster events are emitted by calling
yield from on the event stream returned by stream().
manifest (Union[Mapping[str, Any], str, Path]) – The contents of a manifest.json file or the path to a manifest.json file. A manifest.json contains a representation of a dbt project (models, tests, macros, etc). We use this representation to create corresponding Dagster assets.
select (str) – A dbt selection string for the models in a project that you want
to include. Defaults to fqn:*.
exclude (Optional[str]) – A dbt selection string for the models in a project that you want to exclude. Defaults to “”.
name (Optional[str]) – The name of the op.
io_manager_key (Optional[str]) – The IO manager key that will be set on each of the returned assets. When other ops are downstream of the loaded assets, the IOManager specified here determines how the inputs to those ops are loaded. Defaults to “io_manager”.
partitions_def (Optional[PartitionsDefinition]) – Defines the set of partition keys that compose the dbt assets.
dagster_dbt_translator (Optional[DagsterDbtTranslator]) – Allows customizing how to map dbt models, seeds, etc. to asset keys and asset metadata.
backfill_policy (Optional[BackfillPolicy]) – If a partitions_def is defined, this determines how to execute backfills that target multiple partitions. If a time window partition definition is used, this parameter defaults to a single-run policy.
op_tags (Optional[Dict[str, Any]]) – A dictionary of tags for the op that computes the assets. Frameworks may expect and require certain metadata to be attached to a op. Values that are not strings will be json encoded and must meet the criteria that json.loads(json.dumps(value)) == value.
required_resource_keys (Optional[Set[str]]) – Set of required resource handles.
project (Optional[DbtProject]) – A DbtProject instance which provides a pointer to the dbt project location and manifest. Not required, but needed to attach code references from model code to Dagster assets.
retry_policy (Optional[RetryPolicy]) – The retry policy for the op that computes the asset.
Examples
Running dbt build for a dbt project:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
yield from dbt.cli(["build"], context=context).stream()
Running dbt commands with flags:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
yield from dbt.cli(["build", "--full-refresh"], context=context).stream()
Running dbt commands with --vars:
import json
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_vars = {"key": "value"}
yield from dbt.cli(["build", "--vars", json.dumps(dbt_vars)], context=context).stream()
Retrieving dbt artifacts after running a dbt command:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_build_invocation = dbt.cli(["build"], context=context)
yield from dbt_build_invocation.stream()
run_results_json = dbt_build_invocation.get_artifact("run_results.json")
Running multiple dbt commands for a dbt project:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
yield from dbt.cli(["run"], context=context).stream()
yield from dbt.cli(["test"], context=context).stream()
Accessing the dbt event stream alongside the Dagster event stream:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_cli_invocation = dbt.cli(["build"], context=context)
# Each dbt event is structured: https://docs.getdbt.com/reference/events-logging
for dbt_event in dbt_invocation.stream_raw_events():
for dagster_event in dbt_event.to_default_asset_events(
manifest=dbt_invocation.manifest,
dagster_dbt_translator=dbt_invocation.dagster_dbt_translator,
context=dbt_invocation.context,
target_path=dbt_invocation.target_path,
):
# Manipulate `dbt_event`
...
# Then yield the Dagster event
yield dagster_event
Customizing the Dagster asset definition metadata inferred from a dbt project using DagsterDbtTranslator:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DagsterDbtTranslator, DbtCliResource, dbt_assets
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
...
@dbt_assets(
manifest=Path("target", "manifest.json"),
dagster_dbt_translator=CustomDagsterDbtTranslator(),
)
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
yield from dbt.cli(["build"], context=context).stream()
Using a custom resource key for dbt:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, my_custom_dbt_resource_key: DbtCliResource):
yield from my_custom_dbt_resource_key.cli(["build"], context=context).stream()
Using a dynamically generated resource key for dbt using required_resource_keys:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
dbt_resource_key = "my_custom_dbt_resource_key"
@dbt_assets(manifest=Path("target", "manifest.json"), required_resource_keys={my_custom_dbt_resource_key})
def my_dbt_assets(context: AssetExecutionContext):
dbt = getattr(context.resources, dbt_resource_key)
yield from dbt.cli(["build"], context=context).stream()
Invoking another Dagster ResourceDefinition alongside dbt:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
from dagster_slack import SlackResource
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource, slack: SlackResource):
yield from dbt.cli(["build"], context=context).stream()
slack_client = slack.get_client()
slack_client.chat_postMessage(channel="#my-channel", text="dbt build succeeded!")
Defining and accessing Dagster Config alongside dbt:
from pathlib import Path
from dagster import AssetExecutionContext, Config
from dagster_dbt import DbtCliResource, dbt_assets
class MyDbtConfig(Config):
full_refresh: bool
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource, config: MyDbtConfig):
dbt_build_args = ["build"]
if config.full_refresh:
dbt_build_args += ["--full-refresh"]
yield from dbt.cli(dbt_build_args, context=context).stream()
Defining Dagster PartitionDefinition alongside dbt:
import json
from pathlib import Path
from dagster import AssetExecutionContext, DailyPartitionDefinition
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(
manifest=Path("target", "manifest.json"),
partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")
)
def partitionshop_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
time_window = context.partition_time_window
dbt_vars = {
"min_date": time_window.start.isoformat(),
"max_date": time_window.end.isoformat()
}
dbt_build_args = ["build", "--vars", json.dumps(dbt_vars)]
yield from dbt.cli(dbt_build_args, context=context).stream()
Holds a set of methods that derive Dagster asset definition metadata given a representation of a dbt resource (models, tests, sources, etc).
This class is exposed so that methods can be overriden to customize how Dagster asset metadata is derived.
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster asset key that represents that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom asset key for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
The Dagster asset key for the dbt resource.
Examples
Adding a prefix to the default asset key generated for each dbt resource:
from typing import Any, Mapping
from dagster import AssetKey
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_asset_key(self, dbt_resource_props: Mapping[str, Any]) -> AssetKey:
return super().get_asset_key(dbt_resource_props).with_prefix("prefix")
Adding a prefix to the default asset key generated for each dbt resource, but only for dbt sources:
from typing import Any, Mapping
from dagster import AssetKey
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_asset_key(self, dbt_resource_props: Mapping[str, Any]) -> AssetKey:
asset_key = super().get_asset_key(dbt_resource_props)
if dbt_resource_props["resource_type"] == "source":
asset_key = asset_key.with_prefix("my_prefix")
return asset_key
( experimental ) This API may break in future versions, even between dot releases.
A function that takes a dictionary representing properties of a dbt resource, and
returns the Dagster dagster.AutoMaterializePolicy for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom auto-materialize policy for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A Dagster auto-materialize policy.
Optional[AutoMaterializePolicy]
Examples
Set a custom auto-materialize policy for all dbt resources:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_auto_materialize_policy(self, dbt_resource_props: Mapping[str, Any]) -> Optional[AutoMaterializePolicy]:
return AutoMaterializePolicy.eager()
Set a custom auto-materialize policy for dbt resources with a specific tag:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_auto_materialize_policy(self, dbt_resource_props: Mapping[str, Any]) -> Optional[AutoMaterializePolicy]:
auto_materialize_policy = None
if "my_custom_tag" in dbt_resource_props.get("tags", []):
auto_materialize_policy = AutoMaterializePolicy.eager()
return auto_materialize_policy
( experimental ) This API may break in future versions, even between dot releases.
A function that takes a dictionary representing properties of a dbt resource, and
returns the Dagster dagster.AutoMaterializePolicy for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom AutomationCondition for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A Dagster auto-materialize policy.
Optional[AutoMaterializePolicy]
Examples
Set a custom AutomationCondition for all dbt resources:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_automation_condition(self, dbt_resource_props: Mapping[str, Any]) -> Optional[AutomationCondition]:
return AutomationCondition.eager()
Set a custom AutomationCondition for dbt resources with a specific tag:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_automation_condition(self, dbt_resource_props: Mapping[str, Any]) -> Optional[AutomationCondition]:
automation_condition = None
if "my_custom_tag" in dbt_resource_props.get("tags", []):
automation_condition = AutomationCondition.eager()
return automation_condition
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster code version for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom code version for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A Dagster code version.
Optional[str]
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_code_version(self, dbt_resource_props: Mapping[str, Any]) -> Optional[str]:
return dbt_resource_props["checksum"]["checksum"]
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster description for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom description for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
The description for the dbt resource.
str
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_description(self, dbt_resource_props: Mapping[str, Any]) -> str:
return "custom description"
( experimental ) This API may break in future versions, even between dot releases.
A function that takes a dictionary representing properties of a dbt resource, and
returns the Dagster dagster.FreshnessPolicy for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom freshness policy for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A Dagster freshness policy.
Optional[FreshnessPolicy]
Examples
Set a custom freshness policy for all dbt resources:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_freshness_policy(self, dbt_resource_props: Mapping[str, Any]) -> Optional[FreshnessPolicy]:
return FreshnessPolicy(maximum_lag_minutes=60)
Set a custom freshness policy for dbt resources with a specific tag:
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_freshness_policy(self, dbt_resource_props: Mapping[str, Any]) -> Optional[FreshnessPolicy]:
freshness_policy = None
if "my_custom_tag" in dbt_resource_props.get("tags", []):
freshness_policy = FreshnessPolicy(maximum_lag_minutes=60)
return freshness_policy
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster group name for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom group name for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A Dagster group name.
Optional[str]
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_group_name(self, dbt_resource_props: Mapping[str, Any]) -> Optional[str]:
return "custom_group_prefix" + dbt_resource_props.get("config", {}).get("group")
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster metadata for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom metadata for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A dictionary representing the Dagster metadata for the dbt resource.
Mapping[str, Any]
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_metadata(self, dbt_resource_props: Mapping[str, Any]) -> Mapping[str, Any]:
return {"custom": "metadata"}
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster owners for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide custom owners for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A set of Dagster owners.
Optional[Sequence[str]]
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_owners(self, dbt_resource_props: Mapping[str, Any]) -> Optional[Sequence[str]]:
return ["user@owner.com", "team:team@owner.com"]
( experimental ) This API may break in future versions, even between dot releases.
A function that takes two dictionaries: the first, representing properties of a dbt resource; and the second, representing the properties of a parent dependency to the first dbt resource. The function returns the Dagster partition mapping for the dbt dependency.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
This method can be overridden to provide a custom partition mapping for a dbt dependency.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt child resource.
dbt_parent_resource_props (Mapping[str, Any]) – A dictionary representing the dbt parent resource, in relationship to the child.
The Dagster partition mapping for the dbt resource. If None is returned, the default partition mapping will be used.
Optional[PartitionMapping]
A function that takes a dictionary representing properties of a dbt resource, and returns the Dagster tags for that resource.
Note that a dbt resource is unrelated to Dagster’s resource concept, and simply represents a model, seed, snapshot or source in a given dbt project. You can learn more about dbt resources and the properties available in this dictionary here: https://docs.getdbt.com/reference/artifacts/manifest-json#resource-details
dbt tags are strings, but Dagster tags are key-value pairs. To bridge this divide, the dbt tag string is used as the Dagster tag key, and the Dagster tag value is set to the empty string, “”.
Any dbt tags that don’t match Dagster’s supported tag key format (e.g. they contain unsupported characters) will be ignored.
This method can be overridden to provide custom tags for a dbt resource.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.
A dictionary representing the Dagster tags for the dbt resource.
Mapping[str, str]
Examples
from typing import Any, Mapping
from dagster_dbt import DagsterDbtTranslator
class CustomDagsterDbtTranslator(DagsterDbtTranslator):
def get_tags(self, dbt_resource_props: Mapping[str, Any]) -> Mapping[str, str]:
return {"custom": "tag"}
Settings to enable Dagster features for your dbt project.
enable_asset_checks (bool) – Whether to load dbt tests as Dagster asset checks. Defaults to True.
enable_duplicate_source_asset_keys (bool) – Whether to allow dbt sources with duplicate Dagster asset keys. Defaults to False.
enable_code_references (bool) – Whether to enable Dagster code references for dbt resources. Defaults to False.
enable_dbt_selection_by_name (bool) – Whether to enable selecting dbt resources by name, rather than fully qualified name. Defaults to False.
Defines a selection of assets from a dbt manifest wrapper and a dbt selection string.
manifest (Mapping[str, Any]) – The dbt manifest blob.
select (str) – A dbt selection string to specify a set of dbt resources.
exclude (Optional[str]) – A dbt selection string to exclude a set of dbt resources.
Examples
import json
from pathlib import Path
from dagster_dbt import DbtManifestAssetSelection
manifest = json.loads(Path("path/to/manifest.json").read_text())
# select the dbt assets that have the tag "foo".
my_selection = DbtManifestAssetSelection(manifest=manifest, select="tag:foo")
Build an asset selection for a dbt selection string.
See https://docs.getdbt.com/reference/node-selection/syntax#how-does-selection-work for more information.
dbt_select (str) – A dbt selection string to specify a set of dbt resources.
dbt_exclude (Optional[str]) – A dbt selection string to exclude a set of dbt resources.
An asset selection for the selected dbt nodes.
Examples
from dagster_dbt import dbt_assets, build_dbt_asset_selection
@dbt_assets(manifest=...)
def all_dbt_assets():
...
# Select the dbt assets that have the tag "foo".
foo_selection = build_dbt_asset_selection([dbt_assets], dbt_select="tag:foo")
# Select the dbt assets that have the tag "foo" and all Dagster assets downstream
# of them (dbt-related or otherwise)
foo_and_downstream_selection = foo_selection.downstream()
Building an asset selection on a dbt assets definition with an existing selection:
from dagster_dbt import dbt_assets, build_dbt_asset_selection
@dbt_assets(
manifest=...
select="bar+",
)
def bar_plus_dbt_assets():
...
# Select the dbt assets that are in the intersection of having the tag "foo" and being
# in the existing selection "bar+".
bar_plus_and_foo_selection = build_dbt_asset_selection(
[bar_plus_dbt_assets],
dbt_select="tag:foo"
)
# Furthermore, select all assets downstream (dbt-related or otherwise).
bar_plus_and_foo_and_downstream_selection = bar_plus_and_foo_selection.downstream()
Build a schedule to materialize a specified set of dbt resources from a dbt selection string.
See https://docs.getdbt.com/reference/node-selection/syntax#how-does-selection-work for more information.
job_name (str) – The name of the job to materialize the dbt resources.
cron_schedule (str) – The cron schedule to define the schedule.
dbt_select (str) – A dbt selection string to specify a set of dbt resources.
dbt_exclude (Optional[str]) – A dbt selection string to exclude a set of dbt resources.
schedule_name (Optional[str]) – The name of the dbt schedule to create.
tags (Optional[Mapping[str, str]]) – A dictionary of tags (string key-value pairs) to attach to the scheduled runs.
config (Optional[RunConfig]) – The config that parameterizes the execution of this schedule.
execution_timezone (Optional[str]) – Timezone in which the schedule should run. Supported strings for timezones are the ones provided by the IANA time zone database <https://www.iana.org/time-zones> - e.g. “America/Los_Angeles”.
A definition to materialize the selected dbt resources on a cron schedule.
Examples
from dagster_dbt import dbt_assets, build_schedule_from_dbt_selection
@dbt_assets(manifest=...)
def all_dbt_assets():
...
daily_dbt_assets_schedule = build_schedule_from_dbt_selection(
[all_dbt_assets],
job_name="all_dbt_assets",
cron_schedule="0 0 * * *",
dbt_select="fqn:*",
)
Return the corresponding Dagster asset key for a dbt model, seed, or snapshot.
dbt_assets (AssetsDefinition) – An AssetsDefinition object produced by @dbt_assets.
model_name (str) – The name of the dbt model, seed, or snapshot.
The corresponding Dagster asset key.
Examples
from dagster import asset
from dagster_dbt import dbt_assets, get_asset_key_for_model
@dbt_assets(manifest=...)
def all_dbt_assets():
...
@asset(deps={get_asset_key_for_model([all_dbt_assets], "customers")})
def cleaned_customers():
...
Returns the corresponding Dagster asset key for a dbt source with a singular table.
source_name (str) – The name of the dbt source.
DagsterInvalidInvocationError – If the source has more than one table.
The corresponding Dagster asset key.
Examples
from dagster import asset
from dagster_dbt import dbt_assets, get_asset_key_for_source
@dbt_assets(manifest=...)
def all_dbt_assets():
...
@asset(key=get_asset_key_for_source([all_dbt_assets], "my_source"))
def upstream_python_asset():
...
Returns the corresponding Dagster asset keys for all tables in a dbt source.
This is a convenience method that makes it easy to define a multi-asset that generates all the tables for a given dbt source.
source_name (str) – The name of the dbt source.
for all tables in the given dbt source.
Mapping[str, AssetKey]
Examples
from dagster import AssetOut, multi_asset
from dagster_dbt import dbt_assets, get_asset_keys_by_output_name_for_source
@dbt_assets(manifest=...)
def all_dbt_assets():
...
@multi_asset(
outs={
name: AssetOut(key=asset_key)
for name, asset_key in get_asset_keys_by_output_name_for_source(
[all_dbt_assets], "raw_data"
).items()
},
)
def upstream_python_asset():
...
Representation of a dbt project and related settings that assist with managing the project preparation.
Using this helps achieve a setup where the dbt manifest file and dbt dependencies are available and up-to-date: * during development, pull the dependencies and reload the manifest at run time to pick up any changes. * when deployed, expect a manifest that was created at build time to reduce start-up time.
The cli dagster-dbt project prepare-and-package can be used as part of the deployment process to
handle the project preparation.
This object can be passed directly to DbtCliResource.
project_dir (Union[str, Path]) – The directory of the dbt project.
target_path (Union[str, Path]) – The path, relative to the project directory, to output artifacts. It corresponds to the target path in dbt. Default: “target”
target (Optional[str]) – The target from your dbt profiles.yml to use for execution, if it should be explicitly set.
packaged_project_dir (Optional[Union[str, Path]]) – A directory that will contain a copy of the dbt project and the manifest.json when the artifacts have been built. The prepare method will handle syncing the project_path to this directory. This is useful when the dbt project needs to be part of the python package data like when deploying using PEX.
state_path (Optional[Union[str, Path]]) – The path, relative to the project directory, to reference artifacts from another run.
Examples
Creating a DbtProject with by referencing the dbt project directory:
from pathlib import Path
from dagster_dbt import DbtProject
my_project = DbtProject(project_dir=Path("path/to/dbt_project"))
Creating a DbtProject that changes target based on environment variables and uses manged state artifacts:
import os
from pathlib import Path
from dagster_dbt import DbtProject
def get_env():
if os.getenv("DAGSTER_CLOUD_IS_BRANCH_DEPLOYMENT", "") == "1":
return "BRANCH"
if os.getenv("DAGSTER_CLOUD_DEPLOYMENT_NAME", "") == "prod":
return "PROD"
return "LOCAL"
dbt_project = DbtProject(
project_dir=Path('path/to/dbt_project'),
state_path="target/managed_state",
target=get_env(),
)
Prepare a dbt project at run time during development, i.e. when dagster dev is used. This method has no effect outside this development context.
The preparation process ensures that the dbt manifest file and dbt dependencies are available and up-to-date. During development, it pulls the dependencies and reloads the manifest at run time to pick up any changes.
If this method returns successfully, self.manifest_path will point to a loadable manifest file. This method causes errors if the manifest file has not been correctly created by the preparation process.
Examples
Preparing a DbtProject during development:
from pathlib import Path
from dagster import Definitions
from dagster_dbt import DbtProject
my_project = DbtProject(project_dir=Path("path/to/dbt_project"))
my_project.prepare_if_dev()
defs = Definitions(
resources={
"dbt": DbtCliResource(project_dir=my_project),
},
...
)
( experimental ) This API may break in future versions, even between dot releases.
Returns a sequence of freshness checks constructed from the provided dbt assets.
Freshness checks can be configured on a per-model basis in the model schema configuration.
For assets which are not partitioned based on time, the freshness check configuration mirrors
that of the build_last_update_freshness_checks() function. lower_bound_delta is provided in
terms of seconds, and deadline_cron is optional.
For time-partitioned assets, the freshness check configuration mirrors that of the
build_time_partition_freshness_checks() function.
Below is example of configuring a non-time-partitioned dbt asset with a freshness check. This code would be placed in the schema.yml file for the dbt model.
models:
- name: customers
...
meta:
dagster:
freshness_check:
lower_bound_delta_seconds: 86400 # 1 day
deadline_cron: "0 0 * * *" # Optional
severity: "WARN" # Optional, defaults to "WARN"
Below is an example of configuring a time-partitioned dbt asset with a freshness check. This code would be placed in the schema.yml file for the dbt model.
models:
- name: customers
...
meta:
dagster:
freshness_check:
deadline_cron: "0 0 * * *"
severity: "WARN" # Optional, defaults to "WARN"
dbt_assets (Sequence[AssetsDefinition]) – A sequence of dbt assets to construct freshness checks from.
freshness checks for the provided dbt assets.
Sequence[AssetChecksDefinition]
A resource used to execute dbt CLI commands.
The path to the dbt project directory. This directory should contain a dbt_project.yml. See https://docs.getdbt.com/reference/dbt_project.yml for more information.
str
A list of global flags configuration to pass to the dbt CLI invocation. Invoke dbt –help to see a full list of global flags.
List[str]
The path to the directory containing your dbt profiles.yml. By default, the current working directory is used, which is the dbt project directory. See https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles for more information.
Optional[str]
The profile from your dbt profiles.yml to use for execution. See https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles for more information.
Optional[str]
The target from your dbt profiles.yml to use for execution. See https://docs.getdbt.com/docs/core/connect-data-platform/connection-profiles for more information.
Optional[str]
The path to the dbt executable. By default, this is dbt.
str
The path, relative to the project directory, to a directory of dbt artifacts to be used with –state / –defer-state.
Optional[str]
Examples
Creating a dbt resource with only a reference to project_dir:
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(project_dir="/path/to/dbt/project")
Creating a dbt resource with a custom profiles_dir:
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(
project_dir="/path/to/dbt/project",
profiles_dir="/path/to/dbt/project/profiles",
)
Creating a dbt resource with a custom profile and target:
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(
project_dir="/path/to/dbt/project",
profiles_dir="/path/to/dbt/project/profiles",
profile="jaffle_shop",
target="dev",
)
Creating a dbt resource with global configs, e.g. disabling colored logs with --no-use-color:
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(
project_dir="/path/to/dbt/project",
global_config_flags=["--no-use-color"],
)
Creating a dbt resource with custom dbt executable path:
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(
project_dir="/path/to/dbt/project",
dbt_executable="/path/to/dbt/executable",
)
Create a subprocess to execute a dbt CLI command.
args (Sequence[str]) – The dbt CLI command to execute.
raise_on_error (bool) – Whether to raise an exception if the dbt CLI command fails.
manifest (Optional[Union[Mapping[str, Any], str, Path]]) – The dbt manifest blob. If an execution context from within @dbt_assets is provided to the context argument, then the manifest provided to @dbt_assets will be used.
dagster_dbt_translator (Optional[DagsterDbtTranslator]) – The translator to link dbt nodes to Dagster assets. If an execution context from within @dbt_assets is provided to the context argument, then the dagster_dbt_translator provided to @dbt_assets will be used.
context (Optional[Union[OpExecutionContext, AssetExecutionContext]]) – The execution context from within @dbt_assets. If an AssetExecutionContext is passed, its underlying OpExecutionContext will be used.
target_path (Optional[Path]) – An explicit path to a target folder to use to store and retrieve dbt artifacts when running a dbt CLI command. If not provided, a unique target path will be generated.
dbt CLI command.
Examples
Streaming Dagster events for dbt asset materializations and observations:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
yield from dbt.cli(["run"], context=context).stream()
Retrieving a dbt artifact after streaming the Dagster events:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_run_invocation = dbt.cli(["run"], context=context)
yield from dbt_run_invocation.stream()
# Retrieve the `run_results.json` dbt artifact as a dictionary:
run_results_json = dbt_run_invocation.get_artifact("run_results.json")
# Retrieve the `run_results.json` dbt artifact as a file path:
run_results_path = dbt_run_invocation.target_path.joinpath("run_results.json")
Customizing the asset materialization metadata when streaming the Dagster events:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_cli_invocation = dbt.cli(["run"], context=context)
for dagster_event in dbt_cli_invocation.stream():
if isinstance(dagster_event, Output):
context.add_output_metadata(
metadata={
"my_custom_metadata": "my_custom_metadata_value",
},
output_name=dagster_event.output_name,
)
yield dagster_event
Suppressing exceptions from a dbt CLI command when a non-zero exit code is returned:
from pathlib import Path
from dagster import AssetExecutionContext
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):
dbt_run_invocation = dbt.cli(["run"], context=context, raise_on_error=False)
if dbt_run_invocation.is_successful():
yield from dbt_run_invocation.stream()
else:
...
Invoking a dbt CLI command in a custom asset or op:
import json
from dagster import Nothing, Out, asset, op
from dagster_dbt import DbtCliResource
@asset
def my_dbt_asset(dbt: DbtCliResource):
dbt_macro_args = {"key": "value"}
dbt.cli(["run-operation", "my-macro", json.dumps(dbt_macro_args)]).wait()
@op(out=Out(Nothing))
def my_dbt_op(dbt: DbtCliResource):
dbt_macro_args = {"key": "value"}
yield from dbt.cli(["run-operation", "my-macro", json.dumps(dbt_macro_args)]).stream()
Build the defer arguments for the dbt CLI command, using the supplied state directory. If no state directory is supplied, or the state directory does not have a manifest for. comparison, an empty list of arguments is returned.
The defer arguments for the dbt CLI command.
Sequence[str]
Build the state arguments for the dbt CLI command, using the supplied state directory. If no state directory is supplied, or the state directory does not have a manifest for. comparison, an empty list of arguments is returned.
The state arguments for the dbt CLI command.
Sequence[str]
The representation of an invoked dbt command.
process (subprocess.Popen) – The process running the dbt command.
manifest (Mapping[str, Any]) – The dbt manifest blob.
project_dir (Path) – The path to the dbt project.
target_path (Path) – The path to the dbt target folder.
raise_on_error (bool) – Whether to raise an exception if the dbt command fails.
Retrieve a dbt artifact from the target path.
See https://docs.getdbt.com/reference/artifacts/dbt-artifacts for more information.
artifact (Union[Literal["manifest.json"], Literal["catalog.json"], Literal["run_results.json"], Literal["sources.json"]]) – The name of the artifact to retrieve.
The artifact as a dictionary.
Dict[str, Any]
Examples
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(project_dir="/path/to/dbt/project")
dbt_cli_invocation = dbt.cli(["run"]).wait()
# Retrieve the run_results.json artifact.
run_results = dbt_cli_invocation.get_artifact("run_results.json")
Return an exception if the dbt CLI process failed.
An exception if the dbt CLI process failed, and None otherwise.
Optional[Exception]
Examples
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(project_dir="/path/to/dbt/project")
dbt_cli_invocation = dbt.cli(["run"], raise_on_error=False)
error = dbt_cli_invocation.get_error()
if error:
logger.error(error)
Return whether the dbt CLI process completed successfully.
True, if the dbt CLI process returns with a zero exit code, and False otherwise.
bool
Examples
from dagster_dbt import DbtCliResource
dbt = DbtCliResource(project_dir="/path/to/dbt/project")
dbt_cli_invocation = dbt.cli(["run"], raise_on_error=False)
if dbt_cli_invocation.is_successful():
...
Stream the events from the dbt CLI process and convert them to Dagster events.
A set of corresponding Dagster events.
In a Dagster asset definition, the following are yielded: - Output for refables (e.g. models, seeds, snapshots.) - AssetCheckResult for dbt test results that are enabled as asset checks. - AssetObservation for dbt test results that are not enabled as asset checks.
In a Dagster op definition, the following are yielded: - AssetMaterialization for dbt test results that are not enabled as asset checks. - AssetObservation for dbt test results.
Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]
Examples
from pathlib import Path
from dagster_dbt import DbtCliResource, dbt_assets
@dbt_assets(manifest=Path("target", "manifest.json"))
def my_dbt_assets(context, dbt: DbtCliResource):
yield from dbt.cli(["run"], context=context).stream()
Stream the events from the dbt CLI process.
An iterator of events from the dbt CLI process.
Iterator[DbtCliEventMessage]
A wrapper around an iterator of dbt events which contains additional methods for post-processing the events, such as fetching row counts for materialized tables.
( experimental ) This API may break in future versions, even between dot releases.
Experimental functionality which will fetch column schema metadata for dbt models in a run once they’re built. It will also fetch schema information for upstream models and generate column lineage metadata using sqlglot, if enabled.
generate_column_lineage (bool) – Whether to generate column lineage metadata using sqlglot.
A set of corresponding Dagster events for dbt models, with column metadata attached, yielded in the order they are emitted by dbt.
Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]
( experimental ) This API may break in future versions, even between dot releases.
Experimental functionality which will fetch row counts for materialized dbt models in a dbt run once they are built. Note that row counts will not be fetched for views, since this requires running the view’s SQL query which may be costly.
A set of corresponding Dagster events for dbt models, with row counts attached, yielded in the order they are emitted by dbt.
Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]
( experimental ) This API may break in future versions, even between dot releases.
Associate each warehouse query with the produced asset materializations for use in Dagster Plus Insights. Currently supports Snowflake and BigQuery.
For more information, see the documentation for dagster_cloud.dagster_insights.dbt_with_snowflake_insights and dagster_cloud.dagster_insights.dbt_with_bigquery_insights.
skip_config_check (bool) – If true, skips the check that the dbt project config is set up correctly. Defaults to False.
record_observation_usage (bool) – If True, associates the usage associated with asset observations with that asset. Default is True.
Example:
@dbt_assets(manifest=DBT_MANIFEST_PATH)
def jaffle_shop_dbt_assets(
context: AssetExecutionContext,
dbt: DbtCliResource,
):
yield from dbt.cli(["build"], context=context).stream().with_insights()
The representation of a dbt CLI event.
raw_event (Dict[str, Any]) – The raw event dictionary. See https://docs.getdbt.com/reference/events-logging#structured-logging for more information.
event_history_metadata (Dict[str, Any]) – A dictionary of metadata about the current event, gathered from previous historical events.
Convert a dbt CLI event to a set of corresponding Dagster events.
manifest (Union[Mapping[str, Any], str, Path]) – The dbt manifest blob.
dagster_dbt_translator (DagsterDbtTranslator) – Optionally, a custom translator for linking dbt nodes to Dagster assets.
context (Optional[Union[OpExecutionContext, AssetExecutionContext]]) – The execution context.
target_path (Optional[Path]) – An explicit path to a target folder used to retrieve dbt artifacts while generating events.
A set of corresponding Dagster events.
In a Dagster asset definition, the following are yielded: - Output for refables (e.g. models, seeds, snapshots.) - AssetCheckResult for dbt test results that are enabled as asset checks. - AssetObservation for dbt test results that are not enabled as asset checks.
In a Dagster op definition, the following are yielded: - AssetMaterialization for dbt test results that are not enabled as asset checks. - AssetObservation for dbt test results.
Iterator[Union[Output, AssetMaterialization, AssetObservation, AssetCheckResult]]
Here, we provide interfaces to manage dbt projects invoked by the hosted dbt Cloud service.
( experimental ) This API may break in future versions, even between dot releases.
Loads a set of dbt models, managed by a dbt Cloud job, into Dagster assets. In order to determine the set of dbt models, the project is compiled to generate the necessary artifacts that define the dbt models and their dependencies.
One Dagster asset is created for each dbt model.
dbt_cloud (ResourceDefinition) – The dbt Cloud resource to use to connect to the dbt Cloud API.
job_id (int) – The ID of the dbt Cloud job to load assets from.
node_info_to_asset_key – (Mapping[str, Any] -> AssetKey): A function that takes a dictionary of dbt metadata and returns the AssetKey that you want to represent a given model or source. By default: dbt model -> AssetKey([model_name]) and dbt source -> AssetKey([source_name, table_name])
node_info_to_group_fn (Dict[str, Any] -> Optional[str]) – A function that takes a dictionary of dbt node info and returns the group that this node should be assigned to.
node_info_to_freshness_policy_fn (Dict[str, Any] -> Optional[FreshnessPolicy]) – A function that takes a dictionary of dbt node info and optionally returns a FreshnessPolicy that should be applied to this node. By default, freshness policies will be created from config applied to dbt models, i.e.: dagster_freshness_policy={“maximum_lag_minutes”: 60, “cron_schedule”: “0 9 * * *”} will result in that model being assigned FreshnessPolicy(maximum_lag_minutes=60, cron_schedule=”0 9 * * *”)
node_info_to_auto_materialize_policy_fn (Dict[str, Any] -> Optional[AutoMaterializePolicy]) – A function that takes a dictionary of dbt node info and optionally returns a AutoMaterializePolicy that should be applied to this node. By default, AutoMaterializePolicies will be created from config applied to dbt models, i.e.: dagster_auto_materialize_policy={“type”: “lazy”} will result in that model being assigned AutoMaterializePolicy.lazy()
node_info_to_definition_metadata_fn (Dict[str, Any] -> Optional[Dict[str, RawMetadataMapping]]) – A function that takes a dictionary of dbt node info and optionally returns a dictionary of metadata to be attached to the corresponding definition. This is added to the default metadata assigned to the node, which consists of the node’s schema (if present).
partitions_def (Optional[PartitionsDefinition]) – ( experimental ) (This parameter may break in future versions, even between dot releases.) Defines the set of partition keys that compose the dbt assets.
partition_key_to_vars_fn (Optional[str -> Dict[str, Any]]) – ( experimental ) (This parameter may break in future versions, even between dot releases.) A function to translate a given partition key (e.g. ‘2022-01-01’) to a dictionary of vars to be passed into the dbt invocation (e.g. {“run_date”: “2022-01-01”})
A definition for the loaded assets.
CacheableAssetsDefinition
Examples
from dagster import repository
from dagster_dbt import dbt_cloud_resource, load_assets_from_dbt_cloud_job
DBT_CLOUD_JOB_ID = 1234
dbt_cloud = dbt_cloud_resource.configured(
{
"auth_token": {"env": "DBT_CLOUD_API_TOKEN"},
"account_id": {"env": "DBT_CLOUD_ACCOUNT_ID"},
}
)
dbt_cloud_assets = load_assets_from_dbt_cloud_job(
dbt_cloud=dbt_cloud, job_id=DBT_CLOUD_JOB_ID
)
@repository
def dbt_cloud_sandbox():
return [dbt_cloud_assets]
The integer ID of the relevant dbt Cloud job. You can find this value by going to the details page of your job in the dbt Cloud UI. It will be the final number in the url, e.g.: https://cloud.getdbt.com/#/accounts/{account_id}/projects/{project_id}/jobs/{job_id}/
The time (in seconds) that will be waited between successive polls.
Default Value: 10
The maximum time that will waited before this operation is timed out. By default, this will never time out.
Default Value: None
If True, materializations corresponding to the results of the dbt operation will be yielded when the op executes.
Default Value: True
If provided and yield_materializations is True, these components will be used to prefix the generated asset keys.
Default Value: [‘dbt’]
Initiates a run for a dbt Cloud job, then polls until the run completes. If the job fails or is otherwised stopped before succeeding, a dagster.Failure exception will be raised, and this op will fail.
It requires the use of a ‘dbt_cloud’ resource, which is used to connect to the dbt Cloud API.
Config Options:
The integer ID of the relevant dbt Cloud job. You can find this value by going to the details
page of your job in the dbt Cloud UI. It will be the final number in the url, e.g.:
https://cloud.getdbt.com/#/accounts/{account_id}/projects/{project_id}/jobs/{job_id}/
The time (in seconds) that will be waited between successive polls. Defaults to 10.
The maximum time (in seconds) that will waited before this operation is timed out. By default, this will never time out.
If True, materializations corresponding to the results of the dbt operation will be
yielded when the solid executes. Defaults to True.
If provided and yield_materializations is True, these components will be used to ” prefix the generated asset keys. Defaults to [“dbt”].
Examples:
from dagster import job
from dagster_dbt import dbt_cloud_resource, dbt_cloud_run_op
my_dbt_cloud_resource = dbt_cloud_resource.configured(
{"auth_token": {"env": "DBT_CLOUD_AUTH_TOKEN"}, "account_id": 77777}
)
run_dbt_nightly_sync = dbt_cloud_run_op.configured(
{"job_id": 54321}, name="run_dbt_nightly_sync"
)
@job(resource_defs={"dbt_cloud": my_dbt_cloud_resource})
def dbt_cloud():
run_dbt_nightly_sync()
This resource helps interact with dbt Cloud connectors.
dbt Cloud API Token. User tokens can be found in the [dbt Cloud UI](https://cloud.getdbt.com/#/profile/api/), or see the [dbt Cloud Docs](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/service-tokens) for instructions on creating a Service Account token.
dbt Cloud Account ID. This value can be found in the url of a variety of views in the dbt Cloud UI, e.g. https://cloud.getdbt.com/#/accounts/{account_id}/settings/.
Specifies if you would like any job that is triggered using this resource to automatically disable its schedule.
Default Value: True
The maximum number of times requests to the dbt Cloud API should be retried before failing.
Default Value: 3
Time (in seconds) to wait between each request retry.
Default Value: 0.25
The hostname where dbt cloud is being hosted (e.g. https://my_org.cloud.getdbt.com/).
Default Value: ‘https://cloud.getdbt.com/’
This resource allows users to programatically interface with the dbt Cloud Administrative REST API (v2) to launch jobs and monitor their progress. This currently implements only a subset of the functionality exposed by the API.
For a complete set of documentation on the dbt Cloud Administrative REST API, including expected response JSON schemae, see the dbt Cloud API Docs.
To configure this resource, we recommend using the configured method.
Examples:
from dagster import job
from dagster_dbt import dbt_cloud_resource
my_dbt_cloud_resource = dbt_cloud_resource.configured(
{
"auth_token": {"env": "DBT_CLOUD_AUTH_TOKEN"},
"account_id": {"env": "DBT_CLOUD_ACCOUNT_ID"},
}
)
@job(resource_defs={"dbt_cloud": my_dbt_cloud_resource})
def my_dbt_cloud_job():
...
Get the group name for a dbt node.
If a Dagster group is configured in the metadata for the node, use that.
Otherwise, if a dbt group is configured for the node, use that.
Get the group name for a dbt node.
Has the same behavior as the default_group_from_dbt_resource_props, except for that, if no group can be determined from config or metadata, falls back to using the subdirectory of the models directory that the source file is in.
dbt_resource_props (Mapping[str, Any]) – A dictionary representing the dbt resource.