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37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import os
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import datahub.emitter.mce_builder as builder
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import datahub.metadata.schema_classes as models
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from datahub.emitter.mcp import MetadataChangeProposalWrapper
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from datahub.emitter.rest_emitter import DatahubRestEmitter
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# Create an emitter to DataHub over REST
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gms_server = os.getenv("DATAHUB_GMS_URL", "http://localhost:8080")
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token = os.getenv("DATAHUB_GMS_TOKEN")
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emitter = DatahubRestEmitter(gms_server=gms_server, token=token)
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dataset_urn = builder.make_dataset_urn(
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name="fct_users_created", platform="hive", env="PROD"
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)
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feature_urn = builder.make_ml_feature_urn(
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feature_table_name="users_feature_table",
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feature_name="user_signup_date",
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)
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# Create feature
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metadata_change_proposal = MetadataChangeProposalWrapper(
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entityUrn=feature_urn,
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aspect=models.MLFeaturePropertiesClass(
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description="Represents the date the user created their account",
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# attaching a source to a feature creates lineage between the feature
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# and the upstream dataset. This is how lineage between your data warehouse
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# and machine learning ecosystem is established.
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sources=[dataset_urn],
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dataType="TIME",
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),
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)
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# Emit metadata!
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emitter.emit_mcp(metadata_change_proposal)
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print(f"Created ML feature: {feature_urn}")
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