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Hi bro, I will look into this and get back to you.

Sure bro, I will do

@dataworldsolution Thanks lot for reply, good videos on fabric

I will provide you, thanks

Sure bro, I will do

Sure

Thanks bro

Hi bro, try with replace function to replace comma with decimal

Try replace function

Thank You brother

I will make video on this

Does it works on Fabric?

Sure I will

Thanks

Thank you

Thanks bro

Sure bro , I will do

Thanks

If you want to automate data ingestion process through ETL than we need ADF option as well, it’s also depend on work process.

@@dataworldsolution Bro , can you elaborate on the work process means

Thanks

Thanks

Thanks

Thanks bro

Sure

Thanks

drive.google.com/drive/folders/1E0rGggx27gZdcfLeR0g5zpP1JqaSbli-?usp=sharing

# python scripts to generate dataset -------------------------------------------------------- import random import csv # Define the categories and subcategories categories = ["Electronics", "Books", "Clothing"] electronics_subcategories = ["Laptops", "Headphones", "Smartphones", "Accessories"] books_subcategories = ["Fiction", "Non-Fiction", "Science"] clothing_subcategories = ["T-Shirts", "Jeans", "Hoodies"] # Function to generate random data for a row def generate_row(): order_id = "ORD" + str(random.randint(1, 10000)).zfill(4) amount = random.randint(50, 500) profit = int(amount * random.uniform(0.1, 0.3)) quantity = random.randint(1, 5) category = random.choice(categories) if category == "Electronics": subcategory = random.choice(electronics_subcategories) elif category == "Books": subcategory = random.choice(books_subcategories) else: subcategory = random.choice(clothing_subcategories) return [order_id, amount, profit, quantity, category, subcategory] # Generate 1000 rows of data rows = [] for _ in range(25): rows.append(generate_row()) # Write the data to a CSV file with open("OrdersAzuredataset2.csv", "w", newline="") as csvfile: writer = csv.writer(csvfile) writer.writerow(["ORDER_ID", "AMOUNT", "PROFIT", "QUANTITY", "CATEGORY", "SUBCATEGORY"]) writer.writerows(rows) print("Dataset created successfully!")

Welcome

Thanks sister

Thanks bro

Busy in some family functions bro , i will prepared also video on ADF Completely , thanks bro

Hi Which statement are you used, look like this is connectivity issue, plz check your data warehouse activated

Thanks

Thanks bro

Thanks bro

Thanks bro

Thanks for watching.

Thanks bro

Hi I am using ADF for automation and transformation with databricks but for add hoc copy command is best. So it depend on type of work.

Bro , do we have to provide access permission to storage for ADF to all the storage accounts we created ?

Sure bro

Bro , use cast function. Or to_date

Thanks for asking

@@dataworldsolution not able to give required format in cast. To_date is also not working - select to_date(datecol,'DD/MM/YYYY'). Can you help with code?

I have python script to create dataset , I will provide you.

use python script to generate dataset :- ------------------ # conda install Faker import csv from faker import Faker import random from datetime import datetime, timedelta fake = Faker() # Define loan statuses loan_statuses = ['PAIDOFF', 'COLLECTION', 'COLLECTION_PAIDOFF'] # Define education levels education_levels = ['High School or Below', 'College', 'Bechalor', 'Master or Above'] # Define genders genders = ['male', 'female'] # Generate 50000 loan payment records num_records = 4000 with open('loan_payment_dataset4.csv', mode='w', newline='') as file: writer = csv.writer(file) writer.writerow(["Loan_ID", "loan_status", "Principal", "terms", "effective_date", "due_date", "paid_off_time", "past_due_days", "age", "education", "Gender"]) for i in range(num_records): loan_id = fake.uuid4() loan_status = random.choice(loan_statuses) principal = str(random.randint(500, 5000)) terms = str(random.randint(7, 30)) + " days" effective_date = fake.date_time_between(start_date="-1y", end_date="now", tzinfo=None) due_date = effective_date + timedelta(days=int(terms.split()[0])) paid_off_time = '' past_due_days = '' if loan_status == 'PAIDOFF': paid_off_time = fake.date_time_between(start_date=effective_date, end_date="now", tzinfo=None) elif loan_status == 'COLLECTION_PAIDOFF': paid_off_time = fake.date_time_between(start_date=due_date, end_date="now", tzinfo=None) past_due_days = str((paid_off_time - due_date).days) elif loan_status == 'COLLECTION': past_due_days = str((datetime.now() - due_date).days) age = str(random.randint(18, 70)) education = random.choice(education_levels) gender = random.choice(genders) writer.writerow([loan_id, loan_status, principal, terms, effective_date, due_date, paid_off_time, past_due_days, age, education, gender]) print("Dataset generation completed.")

Thanks