diff --git a/Scripts/transactions.py b/Scripts/transactions.py
new file mode 100644
index 0000000..87997bc
--- /dev/null
+++ b/Scripts/transactions.py
@@ -0,0 +1,136 @@
+from faker import Faker
+from dotenv import load_dotenv
+from datetime import datetime
+import os
+import random
+import pandas as pd
+import boto3
+import io
+from sqlalchemy import create_engine, text
+from urllib.parse import quote_plus
+
+# ---- Setup ----
+fake = Faker()
+load_dotenv()
+
+# ---- S3 Setup ----
+s3 = boto3.resource(
+    "s3",
+    endpoint_url=os.getenv("STORAGE_ENDPOINT"),
+    aws_access_key_id=os.getenv("STORAGE_ACCESS_KEY"),
+    aws_secret_access_key=os.getenv("STORAGE_SECRET_KEY")
+)
+
+bucket_name = os.getenv("STORAGE_BUCKET")
+accounts_s3_key_parquet = "DataLab/accounts/accounts.parquet"
+transactions_s3_key_parquet = "DataLab/transactions/transactions.parquet"
+
+# ---- Postgres Setup ----
+PG_USER = os.getenv("PG_USER")
+PG_PASSWORD = quote_plus(os.getenv("PG_PASSWORD"))
+PG_HOST = os.getenv("PG_HOST")
+PG_PORT = "5432"
+PG_DB = "postgres"
+
+engine = create_engine(f"postgresql+psycopg2://{PG_USER}:{PG_PASSWORD}@{PG_HOST}:{PG_PORT}/{PG_DB}")
+
+# ---- Ensure local data folder exists ----
+os.makedirs("../Data", exist_ok=True)
+
+# ---- Download accounts parquet from S3 ----
+accounts_df = pd.DataFrame()
+try:
+    obj = s3.Bucket(bucket_name).Object(accounts_s3_key_parquet).get()
+    accounts_df = pd.read_parquet(io.BytesIO(obj['Body'].read()))
+    print("Downloaded accounts.parquet from S3.")
+except s3.meta.client.exceptions.NoSuchKey:
+    print("ERROR: Accounts Parquet not found on S3.")
+    raise SystemExit()
+
+# ---- Sample vendors ----
+vendors = ["Amazon", "Walmart", "Target", "Starbucks", "Apple", "Netflix", "Uber", "Lyft", "BestBuy", "Costco"]
+
+# ---- Helper Functions ----
+def generate_transaction_id(account_id, idx):
+    return f"{account_id}{str(idx).zfill(5)}"
+
+def generate_transaction(account):
+    t_type = random.choices(
+        ["Deposit", "Withdrawal", "Payment", "Transfer"],
+        weights=[0.4, 0.3, 0.2, 0.1], k=1
+    )[0]
+
+    txn = {
+        "transaction_id": None,  # to be filled later
+        "account_id": str(account['account_id']),
+        "branch_id": None,
+        "transaction_type": t_type,
+        "amount": 0,
+        "date": fake.date_between(start_date=pd.to_datetime(account['open_date']), end_date=datetime.today()),
+        "balance_after": 0,
+        "vendor": None,
+        "transaction_location": None
+    }
+
+    if t_type in ["Deposit", "Withdrawal"]:
+        txn["branch_id"] = account.get('branch_id', None)
+        amount = round(random.uniform(50, 7000), 2) if t_type == "Withdrawal" else round(random.uniform(20, 10000), 2)
+        if t_type == "Withdrawal":
+            amount = min(amount, account['balance'])
+            account['balance'] -= amount
+        else:
+            account['balance'] += amount
+        txn["amount"] = amount
+        txn["balance_after"] = round(account['balance'], 2)
+        txn["transaction_location"] = f"Branch {txn['branch_id']}"
+    else:  # Payment / Transfer
+        txn["vendor"] = random.choice(vendors)
+        amount = round(random.uniform(5, 1000), 2)
+        account['balance'] = max(account['balance'] - amount, 0)
+        txn["amount"] = amount
+        txn["balance_after"] = round(account['balance'], 2)
+        txn["transaction_location"] = "POS / Online"
+
+    return txn
+
+# ---- Generate transactions ----
+transactions = []
+idx = 1
+
+for _, account in accounts_df.iterrows():
+    account_transactions_count = random.randint(5, 20)
+    for _ in range(account_transactions_count):
+        txn = generate_transaction(account)
+        txn['transaction_id'] = generate_transaction_id(account['account_id'], idx)
+        transactions.append(txn)
+        idx += 1
+
+transactions_df = pd.DataFrame(transactions)
+
+# ---- Upload / append to S3 as Parquet ----
+try:
+    obj = s3.Bucket(bucket_name).Object(transactions_s3_key_parquet).get()
+    existing_df = pd.read_parquet(io.BytesIO(obj['Body'].read()))
+    combined_df = pd.concat([existing_df, transactions_df], ignore_index=True)
+    print(f"Appended {len(transactions_df)} transactions to existing Parquet on S3.")
+except s3.meta.client.exceptions.NoSuchKey:
+    combined_df = transactions_df
+    print("No existing transactions Parquet on S3, creating new one.")
+
+# Convert to Parquet and upload
+parquet_buffer = io.BytesIO()
+combined_df.to_parquet(parquet_buffer, index=False, engine="pyarrow")
+s3.Bucket(bucket_name).put_object(Key=transactions_s3_key_parquet, Body=parquet_buffer.getvalue())
+print(f"Uploaded combined transactions.parquet to s3://{bucket_name}/{transactions_s3_key_parquet}")
+
+# ---- Append to Postgres ----
+try:
+    combined_df.to_sql("transactions", engine, if_exists="append", index=False)
+    print("Inserted transactions into Postgres successfully!")
+except Exception as e:
+    print("Failed to insert into Postgres:", e)
+
+# ---- Optional: row count check ----
+with engine.connect() as conn:
+    result = conn.execute(text("SELECT COUNT(*) FROM transactions;"))
+    print(f"Rows in transactions table: {result.scalar()}")