In this video I have talked about slowly changing dimension type 2.
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SCD Data:-
customer_dim_data = [
(1,'manish','arwal','india','N','2022-09-15','2022-09-25'),
(2,'vikash','patna','india','Y','2023-08-12',None),
(3,'nikita','delhi','india','Y','2023-09-10',None),
(4,'rakesh','jaipur','india','Y','2023-06-10',None),
(5,'ayush','NY','USA','Y','2023-06-10',None),
(1,'manish','gurgaon','india','Y','2022-09-25',None),
]
customer_schema= ['id','name','city','country','active','effective_start_date','effective_end_date']
customer_dim_df = spark.createDataFrame(data= customer_dim_data,schema=customer_schema)
sales_data = [
(1,1,'manish','2023-01-16','gurgaon','india',380),
(77,1,'manish','2023-03-11','bangalore','india',300),
(12,3,'nikita','2023-09-20','delhi','india',127),
(54,4,'rakesh','2023-08-10','jaipur','india',321),
(65,5,'ayush','2023-09-07','mosco','russia',765),
(89,6,'rajat','2023-08-10','jaipur','india',321)
]
sales_schema = ['sales_id', 'customer_id','customer_name', 'sales_date', 'food_delivery_address','food_delivery_country', 'food_cost']
sales_df = spark.createDataFrame(data=sales_data,schema=sales_schema)
spark.apache.o...
spark.apache.o...
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5 окт 2024
