From 0e05c80ca97234b27090104863dab6e24bd7e96a Mon Sep 17 00:00:00 2001 From: ManishNamburi <76540628+ManishNamburi@users.noreply.github.com> Date: Thu, 25 Sep 2025 11:40:54 -0400 Subject: [PATCH 1/3] Add files via upload A notebook that generates synthetic log in data for various gamers. The gamers can have multiple devices that are consistent across them and are located in different areas. --- .../VideoGameLoginSyntheticDataGeneration.py | 271 ++++++++++++++++++ 1 file changed, 271 insertions(+) create mode 100644 examples/notebooks/VideoGameLoginSyntheticDataGeneration.py diff --git a/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py b/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py new file mode 100644 index 00000000..a9ebc2c4 --- /dev/null +++ b/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py @@ -0,0 +1,271 @@ +# Databricks notebook source +# MAGIC %md +# MAGIC +# MAGIC # Getting Started with the Databricks Labs Data Generator +# MAGIC +# MAGIC This notebook provides an introduction to synthetic data generation using the [Databricks Labs Data Generator (`dbldatagen`)](https://databrickslabs.github.io/dbldatagen/public_docs/index.html). This data generator is useful for generating large synthetic datasets for development, testing, benchmarking, proofs-of-concept, and other use-cases. + +# COMMAND ---------- + +# DBTITLE 1,Install dbldatagen +# MAGIC %pip install dbldatagen + +# COMMAND ---------- + +# DBTITLE 1,Import Modules +import dbldatagen as dg +import dbldatagen.distributions as dist + +from pyspark.sql.types import IntegerType, FloatType, StringType, TimestampType, BooleanType, LongType, ArrayType +from pyspark.sql.functions import current_timestamp + +import random +import string +from datetime import datetime +from pyspark.sql.functions import col, format_string, expr, sha2 + +# COMMAND ---------- + +# DBTITLE 1,Set up Parameters +# Set up how many rows we want along with how many users, devices and IPs we want +ROW_COUNT=4144000 +NUMBER_OF_USERS=200000 +NUMBER_OF_DEVICES= NUMBER_OF_USERS+50000 +NUMBER_OF_IPS=40000 +# ROW_COUNT=1000000000 +# NUMBER_OF_USERS=2000000 +# NUMBER_OF_DEVICES= NUMBER_OF_USERS+500000 +# NUMBER_OF_IPS=400000 +START_TIMESTAMP="2025-03-01 00:00:00" +END_TIMESTAMP="2025-03-30 00:00:00" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC ## Data Generation Specifications +# MAGIC +# MAGIC Let's start by generating a DataFrame with rows representing unique login information. Data generation is controlled by a `DataGenerator` object. Each `DataGenerator` can be extended with rules specifying the output schema and value generation. + +# COMMAND ---------- + +# DBTITLE 1,Generate a DataFrame +default_annotations_spec = ( + dg.DataGenerator( + spark, + name="default_annotations_spec", + rows=ROW_COUNT + ) + .withIdOutput() # Add a unique id column for each row + .withColumn( + "EVENT_TIMESTAMP", + TimestampType(), + data_range=dg.DateRange(START_TIMESTAMP, END_TIMESTAMP, "seconds=1"), + random=True, + ) # Random event timestamp within the specified range + .withColumn( + "internal_ACCOUNTID", + LongType(), + minValue=0x1000000000000, + uniqueValues=NUMBER_OF_USERS, + omit=True, + baseColumnType="hash", + ) # Internal unique account id, omitted from output, used for deterministic hashing + .withColumn( + "ACCOUNTID", StringType(), format="0x%032x", baseColumn="internal_ACCOUNTID" + ) # Public account id as hex string + .withColumn( + "internal_DEVICEID", + LongType(), + minValue=0x1000000000000, + uniqueValues=NUMBER_OF_DEVICES, + omit=True, + baseColumnType="hash", + baseColumn="internal_ACCOUNTID" + ) # Internal device id, unique per account, omitted from output + .withColumn( + "DEVICEID", StringType(), format="0x%032x", baseColumn="internal_DEVICEID" + ) # Public device id as hex string + .withColumn("app_version", StringType(), values=["current"]) # Static app version + .withColumn("authMethod", StringType(), values=["OAuth", "password"]) # Auth method, random selection + # Assign clientName based on DEVICEID deterministically + .withColumn( + "clientName", + StringType(), + expr=""" + element_at( + array('SwitchGameClient','XboxGameClient','PlaystationGameClient','PCGameClient'), + (pmod(abs(hash(DEVICEID)), 4) + 1) + ) + """ + ) + .withColumn( + "clientId", + StringType(), + expr="sha2(concat(ACCOUNTID, clientName), 256)", + baseColumn=["ACCOUNTID", "clientName"] + ) # Deterministic clientId based on ACCOUNTID and clientName + .withColumn( + "correlationId", + StringType(), + expr="sha2(concat(ACCOUNTID, clientId), 256)", + ) # Session correlation id, deterministic hash + .withColumn("country", StringType(), values=["USA", "UK", "AUS"], weights=[0.6, 0.2, 0.2], baseColumn="ACCOUNTID", random=True) # Assign country with 60% USA, 20% UK, 20% AUS + .withColumn("environment", StringType(), values=["prod"]) # Static environment value + .withColumn("EVENT_TYPE", StringType(), values=["account_login_success"]) # Static event type + # Assign geoip_city_name based on country and ACCOUNTID + .withColumn( + "CITY", + StringType(), + expr=""" + CASE + WHEN country = 'USA' THEN element_at(array('New York', 'San Francisco', 'Chicago'), pmod(abs(hash(ACCOUNTID)), 3) + 1) + WHEN country = 'UK' THEN 'London' + WHEN country = 'AUS' THEN 'Sydney' + END + """, + baseColumn=["country", "ACCOUNTID"] + ) + .withColumn("countrycode2", StringType(), expr="CASE WHEN country = 'USA' THEN 'US' WHEN country = 'UK' THEN 'UK' WHEN country = 'AUS' THEN 'AU' END", baseColumn=["country"]) # Country code + # Assign ISP based on country and ACCOUNTID + .withColumn( + "ISP", + StringType(), + expr=""" + CASE + WHEN country = 'USA' THEN element_at(array('Comcast', 'AT&T', 'Verizon', 'Spectrum', 'Cox'), pmod(abs(hash(ACCOUNTID)), 5) + 1) + WHEN country = 'UK' THEN element_at(array('BT', 'Sky', 'Virgin Media', 'TalkTalk', 'EE'), pmod(abs(hash(ACCOUNTID)), 5) + 1) + WHEN country = 'AUS' THEN element_at(array('Telstra', 'Optus', 'TPG', 'Aussie Broadband', 'iiNet'), pmod(abs(hash(ACCOUNTID)), 5) + 1) + ELSE 'Unknown ISP' + END + """, + baseColumn=["country", "ACCOUNTID"] + ) + # Assign latitude based on city + .withColumn( + "latitude", + FloatType(), + expr=""" + CASE + WHEN CITY = 'New York' THEN 40.7128 + WHEN CITY = 'San Francisco' THEN 37.7749 + WHEN CITY = 'Chicago' THEN 41.8781 + WHEN CITY = 'London' THEN 51.5074 + WHEN CITY = 'Sydney' THEN -33.8688 + ELSE 0.0 + END + """, + baseColumn="CITY" + ) + # Assign longitude based on city + .withColumn( + "longitude", + FloatType(), + expr=""" + CASE + WHEN CITY = 'New York' THEN -74.0060 + WHEN CITY = 'San Francisco' THEN -122.4194 + WHEN CITY = 'Chicago' THEN -87.6298 + WHEN CITY = 'London' THEN -0.1278 + WHEN CITY = 'Sydney' THEN 151.2093 + ELSE 0.0 + END + """, + baseColumn="CITY" + ) + # Assign region name based on country and city + .withColumn( + "region_name", + StringType(), + expr=""" + CASE + WHEN country = 'USA' THEN + CASE + WHEN CITY = 'New York' THEN 'New York' + WHEN CITY = 'San Francisco' THEN 'California' + WHEN CITY = 'Chicago' THEN 'Illinois' + ELSE 'Unknown' + END + WHEN country = 'UK' THEN 'England' + WHEN country = 'AUS' THEN 'New South Wales' + ELSE 'Unknown' + END + """, + baseColumn=["country", "CITY"] + ) + # Internal IP address as integer, unique per device, omitted from output + .withColumn( + "internal_REQUESTIPADDRESS", + LongType(), + minValue=0x1000000000000, + uniqueValues=NUMBER_OF_IPS, + omit=True, + baseColumnType="hash", + baseColumn="internal_DEVICEID" + ) + # Convert internal IP integer to dotted quad string + .withColumn( + "REQUESTIPADDRESS", + StringType(), + expr=""" + concat( + cast((internal_REQUESTIPADDRESS >> 24) & 255 as string), '.', + cast((internal_REQUESTIPADDRESS >> 16) & 255 as string), '.', + cast((internal_REQUESTIPADDRESS >> 8) & 255 as string), '.', + cast(internal_REQUESTIPADDRESS & 255 as string) + ) + """, + baseColumn="internal_REQUESTIPADDRESS", + ) + # Generate user agent string using clientName and correlationId + .withColumn( + "userAgent", + StringType(), + expr="concat('Launch/1.0+', clientName, '(', clientName, '/)/', correlationId)", + baseColumn=["clientName", "correlationId"] + ) +) +default_annotations = default_annotations_spec.build() + +# COMMAND ---------- + +# DBTITLE 1,Transform the Dataframe +from pyspark.sql.functions import col, to_date, hour +# Transform the DataFrame +transformed_df = default_annotations.select( + col("EVENT_TYPE").alias("EVENT_TYPE"), + col("EVENT_TIMESTAMP").alias("EVENT_TIMESTAMP"), + hour(col("EVENT_TIMESTAMP")).alias("EVENT_HOUR"), + to_date(col("EVENT_TIMESTAMP")).alias("EVENT_DATE"), + col("ACCOUNTID").alias("ACCOUNTID"), + col("environment").alias("APPENV"), + col("app_version").alias("APP_VERSION"), + col("authMethod").alias("AUTHMETHOD"), + col("clientId").alias("CLIENTID"), + col("clientName").alias("CLIENTNAME"), + col("DEVICEID").alias("DEVICEID"), + col("environment").alias("ENVIRONMENT"), + col("CITY").alias("CITY"), + col("countrycode2").alias("COUNTRY_CODE2"), + col("ISP").alias("ISP"), + col("latitude").cast("double").alias("LATITUDE"), + col("longitude").cast("double").alias("LONGITUDE"), + col("region_name").alias("REGION_NAME"), + col("REQUESTIPADDRESS").alias("REQUESTIPADDRESS"), + col("correlationId").alias("SESSION_ID"), + col("correlationId").alias("TRACKINGUUID"), + col("userAgent").alias("USERAGENT"), +) + +# COMMAND ---------- + +# DBTITLE 1,Look at the Data +display(transformed_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC # Write Data + +# COMMAND ---------- + +transformed_df.write.mode("overwrite").saveAsTable("main.test.EVENT_ACCOUNT_LOGIN_SUCCESS") \ No newline at end of file From 5717660d1897d66f356c29448963095e7a1d73c8 Mon Sep 17 00:00:00 2001 From: ManishNamburi <76540628+ManishNamburi@users.noreply.github.com> Date: Tue, 14 Oct 2025 19:52:33 -0400 Subject: [PATCH 2/3] Update VideoGameLoginSyntheticDataGeneration.py Fixed code with comments from PR --- .../VideoGameLoginSyntheticDataGeneration.py | 167 +++++++++--------- 1 file changed, 81 insertions(+), 86 deletions(-) diff --git a/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py b/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py index a9ebc2c4..54e7e712 100644 --- a/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py +++ b/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py @@ -2,60 +2,58 @@ # MAGIC %md # MAGIC # MAGIC # Getting Started with the Databricks Labs Data Generator -# MAGIC # MAGIC This notebook provides an introduction to synthetic data generation using the [Databricks Labs Data Generator (`dbldatagen`)](https://databrickslabs.github.io/dbldatagen/public_docs/index.html). This data generator is useful for generating large synthetic datasets for development, testing, benchmarking, proofs-of-concept, and other use-cases. +# MAGIC +# MAGIC The notebook simulates data for a user login scenario for the gaming industry. # COMMAND ---------- # DBTITLE 1,Install dbldatagen -# MAGIC %pip install dbldatagen +# dbldatagen can be installed using pip install commands, as a cluster-scoped library, or as a serverless environment-scoped library. +%pip install dbldatagen # COMMAND ---------- # DBTITLE 1,Import Modules import dbldatagen as dg -import dbldatagen.distributions as dist - -from pyspark.sql.types import IntegerType, FloatType, StringType, TimestampType, BooleanType, LongType, ArrayType -from pyspark.sql.functions import current_timestamp -import random -import string -from datetime import datetime -from pyspark.sql.functions import col, format_string, expr, sha2 +from pyspark.sql.types import DoubleType, StringType, TimestampType, LongType +from pyspark.sql.functions import col, expr, sha2, to_date, hour # COMMAND ---------- # DBTITLE 1,Set up Parameters # Set up how many rows we want along with how many users, devices and IPs we want -ROW_COUNT=4144000 -NUMBER_OF_USERS=200000 -NUMBER_OF_DEVICES= NUMBER_OF_USERS+50000 -NUMBER_OF_IPS=40000 -# ROW_COUNT=1000000000 -# NUMBER_OF_USERS=2000000 -# NUMBER_OF_DEVICES= NUMBER_OF_USERS+500000 -# NUMBER_OF_IPS=400000 -START_TIMESTAMP="2025-03-01 00:00:00" -END_TIMESTAMP="2025-03-30 00:00:00" +ROW_COUNT = 4500000 +NUMBER_OF_USERS = 200000 +NUMBER_OF_DEVICES = NUMBER_OF_USERS + 50000 +NUMBER_OF_IPS = 40000 + +START_TIMESTAMP = "2025-03-01 00:00:00" +END_TIMESTAMP = "2025-03-30 00:00:00" # COMMAND ---------- # MAGIC %md # MAGIC ## Data Generation Specifications # MAGIC -# MAGIC Let's start by generating a DataFrame with rows representing unique login information. Data generation is controlled by a `DataGenerator` object. Each `DataGenerator` can be extended with rules specifying the output schema and value generation. +# MAGIC Let's start by generating a DataFrame with rows representing unique login information. Data generation is controlled by a `DataGenerator` object. Each `DataGenerator` can be extended with rules specifying the output schema and value generation. Columns can be defined using `withColumn(...)` with a variety of parameters. +# MAGIC +# MAGIC **colName** – Name of column to add. If this conflicts with the underlying seed column (id), it is recommended that the seed column name is customized during the construction of the data generator spec. +# MAGIC +# MAGIC **colType** – Data type for column. This may be specified as either a type from one of the possible pyspark.sql.types (e.g. StringType, DecimalType(10,3) etc) or as a string containing a Spark SQL type definition (i.e String, array, map) +# MAGIC +# MAGIC **omit** – if True, the column will be omitted from the final set of columns in the generated data. Used to create columns that are used by other columns as intermediate results. Defaults to False +# MAGIC +# MAGIC **expr** – Specifies SQL expression used to create column value. If specified, overrides the default rules for creating column value. Defaults to None +# MAGIC +# MAGIC **baseColumn** – String or list of columns to control order of generation of columns. If not specified, column is dependent on base seed column (which defaults to id) # COMMAND ---------- # DBTITLE 1,Generate a DataFrame default_annotations_spec = ( - dg.DataGenerator( - spark, - name="default_annotations_spec", - rows=ROW_COUNT - ) - .withIdOutput() # Add a unique id column for each row + dg.DataGenerator(spark, name="default_annotations_spec", rows=ROW_COUNT) .withColumn( "EVENT_TIMESTAMP", TimestampType(), @@ -80,38 +78,51 @@ uniqueValues=NUMBER_OF_DEVICES, omit=True, baseColumnType="hash", - baseColumn="internal_ACCOUNTID" - ) # Internal device id, unique per account, omitted from output + baseColumn="internal_ACCOUNTID", + ) # Internal device id, based on account, omitted from output .withColumn( "DEVICEID", StringType(), format="0x%032x", baseColumn="internal_DEVICEID" ) # Public device id as hex string - .withColumn("app_version", StringType(), values=["current"]) # Static app version - .withColumn("authMethod", StringType(), values=["OAuth", "password"]) # Auth method, random selection + .withColumn("APP_VERSION", StringType(), values=["current"]) # Static app version + .withColumn( + "AUTHMETHOD", StringType(), values=["OAuth", "password"] + ) # Auth method, random selection # Assign clientName based on DEVICEID deterministically .withColumn( - "clientName", + "CLIENTNAME", StringType(), expr=""" element_at( array('SwitchGameClient','XboxGameClient','PlaystationGameClient','PCGameClient'), (pmod(abs(hash(DEVICEID)), 4) + 1) ) - """ + """, ) .withColumn( - "clientId", + "CLIENTID", StringType(), - expr="sha2(concat(ACCOUNTID, clientName), 256)", - baseColumn=["ACCOUNTID", "clientName"] + expr="sha2(concat(ACCOUNTID, CLIENTNAME), 256)", + baseColumn=["ACCOUNTID", "CLIENTNAME"], ) # Deterministic clientId based on ACCOUNTID and clientName .withColumn( - "correlationId", + "SESSION_ID", StringType(), - expr="sha2(concat(ACCOUNTID, clientId), 256)", + expr="sha2(concat(ACCOUNTID, CLIENTID), 256)", ) # Session correlation id, deterministic hash - .withColumn("country", StringType(), values=["USA", "UK", "AUS"], weights=[0.6, 0.2, 0.2], baseColumn="ACCOUNTID", random=True) # Assign country with 60% USA, 20% UK, 20% AUS - .withColumn("environment", StringType(), values=["prod"]) # Static environment value - .withColumn("EVENT_TYPE", StringType(), values=["account_login_success"]) # Static event type + .withColumn( + "country", + StringType(), + values=["USA", "UK", "AUS"], + weights=[0.6, 0.2, 0.2], + baseColumn="ACCOUNTID", + random=True, + ) # Assign country with 60% USA, 20% UK, 20% AUS + .withColumn( + "APPENV", StringType(), values=["prod"] + ) # Static environment value + .withColumn( + "EVENT_TYPE", StringType(), values=["account_login_success"] + ) # Static event type # Assign geoip_city_name based on country and ACCOUNTID .withColumn( "CITY", @@ -123,9 +134,14 @@ WHEN country = 'AUS' THEN 'Sydney' END """, - baseColumn=["country", "ACCOUNTID"] + baseColumn=["country", "ACCOUNTID"], ) - .withColumn("countrycode2", StringType(), expr="CASE WHEN country = 'USA' THEN 'US' WHEN country = 'UK' THEN 'UK' WHEN country = 'AUS' THEN 'AU' END", baseColumn=["country"]) # Country code + .withColumn( + "COUNTRY_CODE2", + StringType(), + expr="CASE WHEN country = 'USA' THEN 'US' WHEN country = 'UK' THEN 'UK' WHEN country = 'AUS' THEN 'AU' END", + baseColumn=["country"], + ) # Country code # Assign ISP based on country and ACCOUNTID .withColumn( "ISP", @@ -138,12 +154,12 @@ ELSE 'Unknown ISP' END """, - baseColumn=["country", "ACCOUNTID"] + baseColumn=["country", "ACCOUNTID"], ) # Assign latitude based on city .withColumn( - "latitude", - FloatType(), + "LATITUDE", + DoubleType(), expr=""" CASE WHEN CITY = 'New York' THEN 40.7128 @@ -154,12 +170,12 @@ ELSE 0.0 END """, - baseColumn="CITY" + baseColumn="CITY", ) # Assign longitude based on city .withColumn( - "longitude", - FloatType(), + "LONGITUDE", + DoubleType(), expr=""" CASE WHEN CITY = 'New York' THEN -74.0060 @@ -170,11 +186,11 @@ ELSE 0.0 END """, - baseColumn="CITY" + baseColumn="CITY", ) # Assign region name based on country and city .withColumn( - "region_name", + "REGION_NAME", StringType(), expr=""" CASE @@ -190,7 +206,7 @@ ELSE 'Unknown' END """, - baseColumn=["country", "CITY"] + baseColumn=["country", "CITY"], ) # Internal IP address as integer, unique per device, omitted from output .withColumn( @@ -200,7 +216,7 @@ uniqueValues=NUMBER_OF_IPS, omit=True, baseColumnType="hash", - baseColumn="internal_DEVICEID" + baseColumn="internal_DEVICEID", ) # Convert internal IP integer to dotted quad string .withColumn( @@ -216,50 +232,28 @@ """, baseColumn="internal_REQUESTIPADDRESS", ) - # Generate user agent string using clientName and correlationId + # Generate user agent string using clientName and SESSION_ID .withColumn( - "userAgent", + "USERAGENT", StringType(), - expr="concat('Launch/1.0+', clientName, '(', clientName, '/)/', correlationId)", - baseColumn=["clientName", "correlationId"] + expr="concat('Launch/1.0+', CLIENTNAME, '(', CLIENTNAME, '/)/', SESSION_ID)", + baseColumn=["CLIENTNAME", "SESSION_ID"], ) ) -default_annotations = default_annotations_spec.build() +# Build creates a DataFrame from the DataGenerator +default_logins_df = default_annotations_spec.build() # COMMAND ---------- # DBTITLE 1,Transform the Dataframe -from pyspark.sql.functions import col, to_date, hour -# Transform the DataFrame -transformed_df = default_annotations.select( - col("EVENT_TYPE").alias("EVENT_TYPE"), - col("EVENT_TIMESTAMP").alias("EVENT_TIMESTAMP"), - hour(col("EVENT_TIMESTAMP")).alias("EVENT_HOUR"), - to_date(col("EVENT_TIMESTAMP")).alias("EVENT_DATE"), - col("ACCOUNTID").alias("ACCOUNTID"), - col("environment").alias("APPENV"), - col("app_version").alias("APP_VERSION"), - col("authMethod").alias("AUTHMETHOD"), - col("clientId").alias("CLIENTID"), - col("clientName").alias("CLIENTNAME"), - col("DEVICEID").alias("DEVICEID"), - col("environment").alias("ENVIRONMENT"), - col("CITY").alias("CITY"), - col("countrycode2").alias("COUNTRY_CODE2"), - col("ISP").alias("ISP"), - col("latitude").cast("double").alias("LATITUDE"), - col("longitude").cast("double").alias("LONGITUDE"), - col("region_name").alias("REGION_NAME"), - col("REQUESTIPADDRESS").alias("REQUESTIPADDRESS"), - col("correlationId").alias("SESSION_ID"), - col("correlationId").alias("TRACKINGUUID"), - col("userAgent").alias("USERAGENT"), -) +logins_df = default_logins_df.withColumn( + "EVENT_HOUR", hour(col("EVENT_TIMESTAMP")) +).withColumn("EVENT_DATE", to_date(col("EVENT_TIMESTAMP"))) # COMMAND ---------- # DBTITLE 1,Look at the Data -display(transformed_df) +display(logins_df) # COMMAND ---------- @@ -268,4 +262,5 @@ # COMMAND ---------- -transformed_df.write.mode("overwrite").saveAsTable("main.test.EVENT_ACCOUNT_LOGIN_SUCCESS") \ No newline at end of file + +transformed_df.write.mode("overwrite").saveAsTable("main.test.EVENT_ACCOUNT_LOGIN_SUCCESS") From c263500e5159b0a3053a7f66661eda16720e0a29 Mon Sep 17 00:00:00 2001 From: ManishNamburi <76540628+ManishNamburi@users.noreply.github.com> Date: Tue, 14 Oct 2025 19:53:44 -0400 Subject: [PATCH 3/3] Update and rename VideoGameLoginSyntheticDataGeneration.py to gaming_data_generation.py --- ...eLoginSyntheticDataGeneration.py => gaming_data_generation.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/notebooks/{VideoGameLoginSyntheticDataGeneration.py => gaming_data_generation.py} (100%) diff --git a/examples/notebooks/VideoGameLoginSyntheticDataGeneration.py b/examples/notebooks/gaming_data_generation.py similarity index 100% rename from examples/notebooks/VideoGameLoginSyntheticDataGeneration.py rename to examples/notebooks/gaming_data_generation.py