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One solution could be to use Python, which excels in handling and parsing JSON data, to handle the complex parsing process. Firstly, you can write a script that reads the CSV file and the potentially nested JSON objects in each row. Python’s JSON module (json.loads) can convert JSON data into Python dictionaries, which is a format you can work with more easily. You can then transform the data and segregate it as needed, matching the target structure of your SQL Server tables. Lastly, use a Python SQL toolkit like SQLAlchemy to insert the data into your SQL server tables. You can also handle any issues with data conversion, formatting, and error checking before loading the data into SQL Server tables, making this approach more robust. Of course, remember to test this process on a smaller scale before running it on your full 1 million records data set.
