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Fix notebook format issues
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‎Monitoring-Observability/FabricActivatorRulePipeline/GeneratesRandomData.ipynb‎

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{"cells":[{"cell_type":"code","source":["# Generates Dummy json file in Files/\n","\n","# Import necessary libraries\n","from pyspark.sql import SparkSession\n","from pyspark.sql.types import *\n","import random\n","from datetime import datetime, timedelta\n","\n","# Initialize Spark session (if not already initialized)\n","spark = SparkSession.builder.appName(\"GenerateRandomData\").getOrCreate()\n","\n","# Function to generate random data\n","def generate_random_data(num_entries):\n"," data = []\n"," for i in range(1, num_entries + 1):\n"," name = f\"User{i}\"\n"," entry = {\n"," \"id\": i,\n"," \"name\": name,\n"," \"age\": random.randint(18, 65),\n"," \"email\": f\"{name.lower()}@example.com\",\n"," \"created_at\": (datetime.now() - timedelta(days=random.randint(0, 365))).strftime(\"%Y-%m-%d %H:%M:%S\")\n"," }\n"," data.append(entry)\n"," return data\n","\n","# Generate 10 random entries\n","random_data = generate_random_data(10)\n","\n","# Define schema for the DataFrame\n","schema = StructType([\n"," StructField(\"id\", IntegerType(), True),\n"," StructField(\"name\", StringType(), True),\n"," StructField(\"age\", IntegerType(), True),\n"," StructField(\"email\", StringType(), True),\n"," StructField(\"created_at\", StringType(), True)\n","])\n","\n","# Create a DataFrame from the random data\n","df_random_data = spark.createDataFrame(random_data, schema=schema)\n","\n","# Write the DataFrame to the Lakehouse in the specified path\n","output_path = \"abfss://{WORKSPACE-NAME}@onelake.dfs.fabric.microsoft.com/raw_Bronze.Lakehouse/Files/random_data\" # Replace {WORKSPACE-NAME}\n","df_random_data.write.format(\"delta\").mode(\"overwrite\").save(output_path)\n","\n","print(f\"Random data has been saved to the Lakehouse at '{output_path}'.\")"],"outputs":[],"execution_count":null,"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"8d820f25-3c2e-45b3-8a08-af78f0d45e1d"}],"metadata":{"kernel_info":{"name":"synapse_pyspark"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse PySpark"},"language_info":{"name":"python"},"microsoft":{"language":"python","language_group":"synapse_pyspark","ms_spell_check":{"ms_spell_check_language":"en"}},"nteract":{"version":"[email protected]"},"spark_compute":{"compute_id":"/trident/default","session_options":{"conf":{"spark.synapse.nbs.session.timeout":"1200000"}}},"dependencies":{}},"nbformat":4,"nbformat_minor":5}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d820f25-3c2e-45b3-8a08-af78f0d45e1d",
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"metadata": {
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"microsoft": {
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"language": "python",
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"language_group": "synapse_pyspark"
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}
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},
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"outputs": [],
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"source": [
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"# Generates Dummy json file in Files/\n",
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"\n",
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"# Import necessary libraries\n",
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"from pyspark.sql import SparkSession\n",
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"from pyspark.sql.types import *\n",
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"import random\n",
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"from datetime import datetime, timedelta\n",
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"\n",
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"# Initialize Spark session (if not already initialized)\n",
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"spark = SparkSession.builder.appName(\"GenerateRandomData\").getOrCreate()\n",
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"\n",
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"# Function to generate random data\n",
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"def generate_random_data(num_entries):\n",
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" data = []\n",
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" for i in range(1, num_entries + 1):\n",
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" name = f\"User{i}\"\n",
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" entry = {\n",
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" \"id\": i,\n",
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" \"name\": name,\n",
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" \"age\": random.randint(18, 65),\n",
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" \"email\": f\"{name.lower()}@example.com\",\n",
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" \"created_at\": (datetime.now() - timedelta(days=random.randint(0, 365))).strftime(\"%Y-%m-%d %H:%M:%S\")\n",
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" }\n",
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" data.append(entry)\n",
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" return data\n",
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"\n",
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"# Generate 10 random entries\n",
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"random_data = generate_random_data(10)\n",
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"\n",
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"# Define schema for the DataFrame\n",
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"schema = StructType([\n",
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" StructField(\"id\", IntegerType(), True),\n",
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" StructField(\"name\", StringType(), True),\n",
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" StructField(\"age\", IntegerType(), True),\n",
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" StructField(\"email\", StringType(), True),\n",
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" StructField(\"created_at\", StringType(), True)\n",
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"])\n",
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"\n",
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"# Create a DataFrame from the random data\n",
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"df_random_data = spark.createDataFrame(random_data, schema=schema)\n",
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"\n",
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"# Write the DataFrame to the Lakehouse in the specified path\n",
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"output_path = \"abfss://{WORKSPACE-NAME}@onelake.dfs.fabric.microsoft.com/raw_Bronze.Lakehouse/Files/random_data\" # Replace {WORKSPACE-NAME}\n",
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"df_random_data.write.format(\"delta\").mode(\"overwrite\").save(output_path)\n",
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"\n",
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"print(f\"Random data has been saved to the Lakehouse at '{output_path}'.\")"
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]
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}
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],
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"metadata": {
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"application/vnd.jupyter.widget-state+json": {
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"version": "1.0"
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},
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"dependencies": {},
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"kernel_info": {
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"name": "synapse_pyspark"
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},
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"kernelspec": {
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"display_name": "Synapse PySpark",
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"language": "Python",
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"name": "synapse_pyspark"
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},
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"language_info": {
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"name": "python"
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},
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"microsoft": {
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"language": "python",
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"language_group": "synapse_pyspark",
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"ms_spell_check": {
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"ms_spell_check_language": "en"
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}
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},
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"nteract": {
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"version": "[email protected]"
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},
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"spark_compute": {
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"compute_id": "/trident/default",
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"session_options": {
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"conf": {
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"spark.synapse.nbs.session.timeout": "1200000"
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}
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Workloads-Specific/DataScience/AI_integration/src/fabric-llms-overview_sample.ipynb‎

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}
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],
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"metadata": {
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"application/vnd.jupyter.widget-state+json": {
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"version": "1.0"
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},
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"dependencies": {
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"environment": {
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"environmentId": "766562be-9e21-456c-b270-cac7e4bf8d18",

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