This library provides an integration with Datahub, to support pushing metadata to Datahub from within Dagster ops.
We use the Datahub Python Library. To use it, you’ll first need to start up a Datahub Instance. Datahub Quickstart Guide.
Datahub GMS Server
Personal Access Token
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: False
Base class for Dagster resources that utilize structured config.
This class is a subclass of both ResourceDefinition
and Config
.
Example definition:
class WriterResource(ConfigurableResource):
prefix: str
def output(self, text: str) -> None:
print(f"{self.prefix}{text}")
Example usage:
@asset
def asset_that_uses_writer(writer: WriterResource):
writer.output("text")
defs = Definitions(
assets=[asset_that_uses_writer],
resources={"writer": WriterResource(prefix="a_prefix")},
)
You can optionally use this class to model configuration only and vend an object of a different type for use at runtime. This is useful for those who wish to have a separate object that manages configuration and a separate object at runtime. Or where you want to directly use a third-party class that you do not control.
To do this you override the create_resource methods to return a different object.
class WriterResource(ConfigurableResource):
str: prefix
def create_resource(self, context: InitResourceContext) -> Writer:
# Writer is pre-existing class defined else
return Writer(self.prefix)
Example usage:
@asset
def use_preexisting_writer_as_resource(writer: ResourceParam[Writer]):
writer.output("text")
defs = Definitions(
assets=[use_preexisting_writer_as_resource],
resources={"writer": WriterResource(prefix="a_prefix")},
)
Kafka Boostrap Servers. Comma delimited
Schema Registry Location.
Extra Schema Registry Config.
{}
{
"MetadataChangeEvent": "MetadataChangeEvent_v4",
"MetadataChangeProposal": "MetadataChangeProposal_v1"
}
Base class for Dagster resources that utilize structured config.
This class is a subclass of both ResourceDefinition
and Config
.
Example definition:
class WriterResource(ConfigurableResource):
prefix: str
def output(self, text: str) -> None:
print(f"{self.prefix}{text}")
Example usage:
@asset
def asset_that_uses_writer(writer: WriterResource):
writer.output("text")
defs = Definitions(
assets=[asset_that_uses_writer],
resources={"writer": WriterResource(prefix="a_prefix")},
)
You can optionally use this class to model configuration only and vend an object of a different type for use at runtime. This is useful for those who wish to have a separate object that manages configuration and a separate object at runtime. Or where you want to directly use a third-party class that you do not control.
To do this you override the create_resource methods to return a different object.
class WriterResource(ConfigurableResource):
str: prefix
def create_resource(self, context: InitResourceContext) -> Writer:
# Writer is pre-existing class defined else
return Writer(self.prefix)
Example usage:
@asset
def use_preexisting_writer_as_resource(writer: ResourceParam[Writer]):
writer.output("text")
defs = Definitions(
assets=[use_preexisting_writer_as_resource],
resources={"writer": WriterResource(prefix="a_prefix")},
)
Datahub GMS Server
Personal Access Token
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: None
Default Value: False
Kafka Boostrap Servers. Comma delimited
Schema Registry Location.
Extra Schema Registry Config.
{}
{
"MetadataChangeEvent": "MetadataChangeEvent_v4",
"MetadataChangeProposal": "MetadataChangeProposal_v1"
}