One approach could involve using Azure Data Lake as an intermediary. You can partition your PySpark DataFrames and load them into Azure Data Lake, which is optimized for large-scale data storage and integrates well with PySpark. Once the data is in A...
To clear all objects generated or updated by the DLT pipeline, you can drop the tables manually using the DROP command as you've mentioned. However, to get a completely clean slate, including metadata like the tracking of already processed files in t...
