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michaeldeongreenmichaeldeongreen
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Revert workspace bindings to dev for PR
Generated by 'python scripts/workspace_swap.py --swap-to-dev'. Reverts the semantic model and notebook from michael-green-demo back to dev, removes the feature value set, and unregisters it from settings.json. Required by the check-pr-ready workflow.
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Lines changed: 118 additions & 133 deletions

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‎data/fabric/Import_Patterns_Data.Notebook/notebook-content.py‎

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# Fabric notebook source
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# METADATA ********************
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# META {
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# META "kernel_info": {
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# META "name": "synapse_pyspark"
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# META },
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# META "dependencies": {
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# META "lakehouse": {
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# META "default_lakehouse": "1b80046f-d76e-4748-acfc-575f3fbd23f4",
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# META "default_lakehouse_name": "PatternsLakehouse",
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# META "default_lakehouse_workspace_id": "613e22cd-308a-4106-9745-58bd5164568a",
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# META "known_lakehouses": [
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# META {
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# META "id": "1b80046f-d76e-4748-acfc-575f3fbd23f4"
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# META }
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# META ]
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# META }
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# META }
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# META }
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# CELL ********************
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# Import required libraries for schema definition
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from pyspark.sql.types import StructType, StructField, IntegerType, StringType, DateType, TimestampType
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from datetime import date, datetime
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# Define table names for the Healthcare industry domain
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# These 3 tables form a relational model: patients and doctors linked through appointments
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PATIENTS_TABLE = "patients"
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DOCTORS_TABLE = "doctors"
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APPOINTMENTS_TABLE = "appointments"
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# -------------------------------------------------------
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# Define schemas explicitly — reused for both table creation and data insertion
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# to ensure type consistency and avoid merge field errors
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# -------------------------------------------------------
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patients_schema = StructType([
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StructField("patient_id", IntegerType(), False),
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StructField("first_name", StringType(), False),
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StructField("last_name", StringType(), False),
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StructField("date_of_birth", TimestampType(), True),
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StructField("gender", StringType(), True),
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StructField("phone_number", StringType(), True),
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StructField("email", StringType(), True)
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])
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doctors_schema = StructType([
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StructField("doctor_id", IntegerType(), False),
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StructField("first_name", StringType(), False),
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StructField("last_name", StringType(), False),
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StructField("specialty", StringType(), True),
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StructField("phone_number", StringType(), True),
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StructField("email", StringType(), True)
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])
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appointments_schema = StructType([
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StructField("appointment_id", IntegerType(), False),
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StructField("patient_id", IntegerType(), False),
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StructField("doctor_id", IntegerType(), False),
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StructField("appointment_date", TimestampType(), False),
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StructField("reason", StringType(), True),
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StructField("status", StringType(), True)
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])
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# -------------------------------------------------------
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# Insert data using saveAsTable which writes Delta files AND registers
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# tables in the Lakehouse metastore. The default Lakehouse is attached
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# via the notebook's META dependencies block, so simple table names resolve
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# correctly. Per Fabric docs: df.write.mode("overwrite").format("delta").saveAsTable(name)
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# -------------------------------------------------------
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patients_data = [
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(1, "Alice", "Johnson", datetime(1985, 3, 15), "Female", "555-0101", "alice.johnson@email.com"),
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(2, "Bob", "Smith", datetime(1990, 7, 22), "Male", "555-0102", "bob.smith@email.com"),
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(3, "Carol", "Williams", datetime(1978, 11, 8), "Female", "555-0103", "carol.williams@email.com"),
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(4, "David", "Brown", datetime(2001, 1, 30), "Male", "555-0104", "david.brown@email.com"),
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(5, "Eva", "Davis", datetime(1995, 6, 12), "Female", "555-0105", "eva.davis@email.com")
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]
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patients_df = spark.createDataFrame(patients_data, patients_schema)
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patients_df.write.format("delta").mode("overwrite").saveAsTable(PATIENTS_TABLE)
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print(f"Inserted {patients_df.count()} rows into '{PATIENTS_TABLE}'.")
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doctors_data = [
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(1, "Sarah", "Mitchell", "Cardiology", "555-0201", "sarah.mitchell@hospital.com"),
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(2, "James", "Anderson", "Neurology", "555-0202", "james.anderson@hospital.com"),
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(3, "Emily", "Thompson", "Pediatrics", "555-0203", "emily.thompson@hospital.com")
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]
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doctors_df = spark.createDataFrame(doctors_data, doctors_schema)
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doctors_df.write.format("delta").mode("overwrite").saveAsTable(DOCTORS_TABLE)
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print(f"Inserted {doctors_df.count()} rows into '{DOCTORS_TABLE}'.")
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appointments_data = [
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(1, 1, 1, datetime(2026, 4, 10), "Annual checkup", "Completed"),
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(2, 2, 2, datetime(2026, 4, 11), "Headache consultation", "Completed"),
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(3, 3, 3, datetime(2026, 4, 12), "Child wellness visit", "Scheduled"),
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(4, 4, 1, datetime(2026, 4, 15), "Chest pain follow-up", "Scheduled"),
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(5, 5, 2, datetime(2026, 4, 18), "Neurological evaluation", "Scheduled"),
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(6, 1, 3, datetime(2026, 4, 20), "Flu symptoms", "Scheduled")
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]
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appointments_df = spark.createDataFrame(appointments_data, appointments_schema)
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appointments_df.write.format("delta").mode("overwrite").saveAsTable(APPOINTMENTS_TABLE)
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print(f"Inserted {appointments_df.count()} rows into '{APPOINTMENTS_TABLE}'.")
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print("All tables created and data loaded successfully.")
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# METADATA ********************
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# META {
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# META "language": "python",
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# META "language_group": "synapse_pyspark"
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# META }
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# Fabric notebook source
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# METADATA ********************
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# META {
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# META "kernel_info": {
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# META "name": "synapse_pyspark"
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# META },
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# META "dependencies": {
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# META "lakehouse": {
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# META "default_lakehouse": "c185283c-9dd9-4e40-a17c-aa6303e3a2e9",
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# META "default_lakehouse_name": "PatternsLakehouse",
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# META "default_lakehouse_workspace_id": "d7270f11-feba-4990-baa6-d45e47f23737",
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# META "known_lakehouses": [
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# META {
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# META "id": "c185283c-9dd9-4e40-a17c-aa6303e3a2e9"
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# META }
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# META ]
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# META }
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# META }
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# META }
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# CELL ********************
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# Import required libraries for schema definition
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from pyspark.sql.types import StructType, StructField, IntegerType, StringType, DateType, TimestampType
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from datetime import date, datetime
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# Define table names for the Healthcare industry domain
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# These 3 tables form a relational model: patients and doctors linked through appointments
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PATIENTS_TABLE = "patients"
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DOCTORS_TABLE = "doctors"
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APPOINTMENTS_TABLE = "appointments"
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# -------------------------------------------------------
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# Define schemas explicitly — reused for both table creation and data insertion
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# to ensure type consistency and avoid merge field errors
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# -------------------------------------------------------
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patients_schema = StructType([
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StructField("patient_id", IntegerType(), False),
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StructField("first_name", StringType(), False),
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StructField("last_name", StringType(), False),
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StructField("date_of_birth", TimestampType(), True),
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StructField("gender", StringType(), True),
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StructField("phone_number", StringType(), True),
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StructField("email", StringType(), True)
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])
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doctors_schema = StructType([
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StructField("doctor_id", IntegerType(), False),
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StructField("first_name", StringType(), False),
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StructField("last_name", StringType(), False),
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StructField("specialty", StringType(), True),
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StructField("phone_number", StringType(), True),
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StructField("email", StringType(), True)
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])
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appointments_schema = StructType([
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StructField("appointment_id", IntegerType(), False),
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StructField("patient_id", IntegerType(), False),
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StructField("doctor_id", IntegerType(), False),
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StructField("appointment_date", TimestampType(), False),
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StructField("reason", StringType(), True),
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StructField("status", StringType(), True)
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])
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# -------------------------------------------------------
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# Insert data using saveAsTable which writes Delta files AND registers
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# tables in the Lakehouse metastore. The default Lakehouse is attached
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# via the notebook's META dependencies block, so simple table names resolve
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# correctly. Per Fabric docs: df.write.mode("overwrite").format("delta").saveAsTable(name)
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# -------------------------------------------------------
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patients_data = [
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(1, "Alice", "Johnson", datetime(1985, 3, 15), "Female", "555-0101", "alice.johnson@email.com"),
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(2, "Bob", "Smith", datetime(1990, 7, 22), "Male", "555-0102", "bob.smith@email.com"),
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(3, "Carol", "Williams", datetime(1978, 11, 8), "Female", "555-0103", "carol.williams@email.com"),
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(4, "David", "Brown", datetime(2001, 1, 30), "Male", "555-0104", "david.brown@email.com"),
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(5, "Eva", "Davis", datetime(1995, 6, 12), "Female", "555-0105", "eva.davis@email.com")
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]
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patients_df = spark.createDataFrame(patients_data, patients_schema)
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patients_df.write.format("delta").mode("overwrite").saveAsTable(PATIENTS_TABLE)
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print(f"Inserted {patients_df.count()} rows into '{PATIENTS_TABLE}'.")
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doctors_data = [
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(1, "Sarah", "Mitchell", "Cardiology", "555-0201", "sarah.mitchell@hospital.com"),
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(2, "James", "Anderson", "Neurology", "555-0202", "james.anderson@hospital.com"),
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(3, "Emily", "Thompson", "Pediatrics", "555-0203", "emily.thompson@hospital.com")
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]
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doctors_df = spark.createDataFrame(doctors_data, doctors_schema)
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doctors_df.write.format("delta").mode("overwrite").saveAsTable(DOCTORS_TABLE)
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print(f"Inserted {doctors_df.count()} rows into '{DOCTORS_TABLE}'.")
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appointments_data = [
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(1, 1, 1, datetime(2026, 4, 10), "Annual checkup", "Completed"),
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(2, 2, 2, datetime(2026, 4, 11), "Headache consultation", "Completed"),
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(3, 3, 3, datetime(2026, 4, 12), "Child wellness visit", "Scheduled"),
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(4, 4, 1, datetime(2026, 4, 15), "Chest pain follow-up", "Scheduled"),
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(5, 5, 2, datetime(2026, 4, 18), "Neurological evaluation", "Scheduled"),
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(6, 1, 3, datetime(2026, 4, 20), "Flu symptoms", "Scheduled")
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]
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appointments_df = spark.createDataFrame(appointments_data, appointments_schema)
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appointments_df.write.format("delta").mode("overwrite").saveAsTable(APPOINTMENTS_TABLE)
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print(f"Inserted {appointments_df.count()} rows into '{APPOINTMENTS_TABLE}'.")
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print("All tables created and data loaded successfully.")
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# METADATA ********************
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# META {
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# META "language": "python",
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# META "language_group": "synapse_pyspark"
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# META }

‎data/fabric/Patterns_Semantic_Model.SemanticModel/definition/expressions.tmdl‎

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expression 'DirectLake - PatternsLakehouse' =
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let
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Source = AzureStorage.DataLake("https://onelake.dfs.fabric.microsoft.com/613e22cd-308a-4106-9745-58bd5164568a/1b80046f-d76e-4748-acfc-575f3fbd23f4", [HierarchicalNavigation=true])
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Source = AzureStorage.DataLake("https://onelake.dfs.fabric.microsoft.com/d7270f11-feba-4990-baa6-d45e47f23737/c185283c-9dd9-4e40-a17c-aa6303e3a2e9", [HierarchicalNavigation=true])
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in
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Source
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lineageTag: 67c45651-79f4-4e05-97d9-0430dcddb756

‎data/fabric/Patterns_Variables.VariableLibrary/settings.json‎

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"$schema": "https://developer.microsoft.com/json-schemas/fabric/item/variableLibrary/definition/settings/1.0.0/schema.json",
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"valueSetsOrder": [
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"Test",
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"Prod",
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"michael-green-demo"
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"Prod"
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]
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}

‎data/fabric/Patterns_Variables.VariableLibrary/valueSets/michael-green-demo.json‎

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