data.py

# Databricks Notebook Example (Safe for GitHub)

# Import necessary libraries (if any)
# Note:  Avoid including actual secrets (API keys, passwords) directly in your code.
#       Use Databricks secrets management instead.

# Example Data Transformation
def transform_data(df):
    """
    This function performs a simple data transformation.
    It's designed to be safe for committing to GitHub as it doesn't contain
    any sensitive information or complex logic that might introduce
    security vulnerabilities.
    """
    try:
        # Example: Add a new column (replace with your actual logic)
        df = df.withColumn("new_column", df["existing_column"] * 2)  # Example transformation
        return df
    except Exception as e:
        print(f"Error during transformation: {e}")
        return df  # Return the original DataFrame in case of error

# Sample DataFrame (replace with your actual data source)
data = [("A", 10), ("B", 20), ("C", 30)]
columns = ["existing_column", "value"]
df = spark.createDataFrame(data, columns)

# Perform the data transformation
transformed_df = transform_data(df)

# Display the transformed data (for testing)
transformed_df.display()

# Example: Write data to a safe location (avoid hardcoding paths)
# Use Databricks file system (DBFS) or a cloud storage location (configured securely)
output_path = "/mnt/my_safe_location/transformed_data"  # Replace with a safe, configured path
transformed_df.write.parquet(output_path, mode="overwrite")

print(f"Transformed data written to: {output_path}")

# Example: Log a message (useful for monitoring)
print("Data transformation and write operation completed successfully.")

# Important Security Considerations for GitHub:

# 1. Secrets Management:  NEVER hardcode API keys, passwords, or other sensitive information in your code.
#    Use Databricks Secrets Management to store and access secrets securely.

# 2. Input Validation: If your code takes user input, make sure to validate it thoroughly
#    to prevent injection attacks.

# 3. Dependency Management: Keep your library dependencies up-to-date to avoid known vulnerabilities.
#    Use a requirements.txt file or similar to manage dependencies.

# 4. Secure Output Paths:  Avoid hardcoding output paths that might expose sensitive data.
#    Use secure, configured storage locations.

# 5. Code Reviews: Have your code reviewed by other team members to catch potential security issues.

# 6. Avoid Complex Logic in GitHub: If you have very complex or security-sensitive logic, consider
#    keeping it out of GitHub and managing it separately (e.g., using Databricks jobs).  Focus on
#    the parts of your code that are safe to share in a version control system.

# 7. Test Thoroughly:  Test your code thoroughly to ensure it works as expected and doesn't
#    introduce any security vulnerabilities.

# 8. Regularly Scan: Use tools like Prisma Cloud to regularly scan your GitHub repositories and
#    Databricks environments for security issues.