mo
944635ffe1
Source generators (postgres/mysql/mongodb) now emit realistic PII (name/email/phone/ip/iban/address/national_id/dob). deploy/mask_pii_setup.py ALTERs the source tables and backfills a bounded sample so Debezium CDC and OpenMetadata PII auto-classification see real sensitive values.
104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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Generate fake employee event data for MySQL.
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Light + configurable via GEN_ROWS. Now also populates realistic PII columns
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(employee_name/email/phone, national_id, home_address, date_of_birth).
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"""
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import mysql.connector
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import random
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from datetime import datetime, timedelta
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import uuid
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import os
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DB_HOST = "10.0.21.51"
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DB_PORT = "3306"
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DB_NAME = "hr"
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DB_USER = "mo"
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DB_PASSWORD = "Dell2026!"
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TARGET_ROWS = int(os.getenv("GEN_ROWS", "5000"))
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BATCH_SIZE = min(10000, max(500, TARGET_ROWS))
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DEPARTMENTS = ["HR", "Operations", "Sales", "Marketing", "Finance", "IT", "Engineering", "Legal"]
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ROLE_NAMES = ["Analyst", "Lead", "Manager", "Consultant", "Director", "Engineer", "Specialist", "Coordinator"]
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REGIONS = ["EU", "APAC", "LATAM", "NA", "EMEA"]
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EVENT_TYPES = ["TRANSFER", "PROMOTION", "TERMINATION", "HIRED", "SALARY_CHANGE", "DEPARTMENT_CHANGE"]
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FIRST = ["Sophie", "Liam", "Emma", "Noah", "Julia", "Lucas", "Mila", "Daan", "Anna", "Sem",
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"Eva", "Finn", "Tess", "Bram", "Lotte", "Max", "Sara", "Thijs", "Nina", "Ruben"]
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LAST = ["de Vries", "Jansen", "Bakker", "Visser", "Smit", "Meijer", "Mulder", "Bos",
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"Vos", "Peters", "Hendriks", "Dijkstra", "Kok", "Willems", "Maas", "Koning"]
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STREETS = ["Kerkstraat", "Dorpsweg", "Molenpad", "Stationsplein", "Industrieweg", "Lindelaan",
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"Beukenhof", "Havenstraat", "Parkweg", "Schoolstraat"]
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CITIES = ["Amsterdam", "Rotterdam", "Utrecht", "Eindhoven", "Den Haag", "Groningen", "Tilburg", "Almere"]
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DOMAINS = ["example.com", "mail.nl", "acme-corp.eu", "company.eu", "contact.io"]
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def _name():
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return f"{random.choice(FIRST)} {random.choice(LAST)}"
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def _pii():
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nm = _name()
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return (
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nm,
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f"{nm.lower().replace(' ', '.')}{random.randint(1, 999)}@{random.choice(DOMAINS)}",
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f"+31 6 {random.randint(10, 99)} {random.randint(100000, 999999)}",
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str(random.randint(100000000, 999999999)),
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f"{random.choice(STREETS)} {random.randint(1, 320)}, {random.randint(1000, 9999)} {random.choice(CITIES)}",
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f"{random.randint(1960, 2002)}-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}",
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)
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def generate_fake_employee_event():
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employee_id = random.randint(1, 100000)
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department = random.choice(DEPARTMENTS)
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role_name = random.choice(ROLE_NAMES)
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region = random.choice(REGIONS)
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event_type = random.choice(EVENT_TYPES)
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days_ago = random.randint(0, 730)
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event_ts = datetime.now() - timedelta(days=days_ago, hours=random.randint(0, 23),
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minutes=random.randint(0, 59))
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salary_change = round(random.uniform(1000.0, 20000.0), 2) if random.random() > 0.3 else None
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notes = str(uuid.uuid4()) * 10
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name, email, phone, nid, address, dob = _pii()
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return (employee_id, department, role_name, region, event_type, salary_change, event_ts, notes,
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name, email, phone, nid, address, dob)
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INSERT_SQL = """
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INSERT INTO employee_events (employee_id, department, role_name, region,
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event_type, salary_change, event_ts, notes,
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employee_name, employee_email, employee_phone,
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national_id, home_address, date_of_birth)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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"""
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def main():
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print(f"Connecting to MySQL at {DB_HOST}:{DB_PORT}...")
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conn = mysql.connector.connect(host=DB_HOST, port=DB_PORT, database=DB_NAME, user=DB_USER, password=DB_PASSWORD)
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cursor = conn.cursor()
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print(f"Generating {TARGET_ROWS} employee events (with PII)... batch={BATCH_SIZE}")
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total_generated = 0
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batch = []
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for i in range(TARGET_ROWS):
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batch.append(generate_fake_employee_event())
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if len(batch) >= BATCH_SIZE:
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cursor.executemany(INSERT_SQL, batch)
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conn.commit()
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total_generated += len(batch)
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batch = []
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if batch:
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cursor.executemany(INSERT_SQL, batch)
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conn.commit()
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total_generated += len(batch)
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cursor.close()
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conn.close()
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print(f"Completed! Generated {total_generated} employee events.")
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if __name__ == "__main__":
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main()
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