I was able to run the following code without issues. df = spark.read.format("csv").option("header","true").load("Files/data/orders_data/2019.csv") But when I try to create a file using python it doesn't let me and throws FileNotFoundError: [Errno 2] No such file or directory: '/Files/xyz/2024-11-03_xyz.json' import json data = "{'k1':'xyz'}" file_path = f'/Files/xyz/{start_date}_xyz.json' # Open the file in write mode ('w') and save the JSON data with open(file_path, 'w') as file: # The `json.dump` method serializes `data` as a JSON formatted stream to `file` `indent=4` makes the file human-readable by adding whitespace json.dump(data, file, indent=4) print(f"Data successfully saved to {file_path}") Isn't it possible to access and interact with lakehouse via simple Python code?

Morning, I'm currently in the middle of a Fabric trial where we're trying out a bunch of use cases. We've just hit a bit of a hurdle and I am wondering if there is already a guide available somewhere. We've created a basic star schema in a warehouse (that we've called our Gold Warehouse) and loaded some data in. Initially in our pipeline we were just dropping and recreating the tables (for ease) using CTAS statements.. Then when we went on to playing around with the semantic model, that broke our pipeline because of the FKs it created. This is where we hit a bit of a dead end. We're trying to resist using DELETE statements for speed reasons. But techniques such as programmatically dropping the constraints and re-adding them or partition switching seem to be off the table. Curious to know if there is a guide or what everyone else is doing?