Since the datasets are large, it is obvious that these things happen.

There are a few things you can do to help make importing larger datasets into MySQL more efficient:

Indexing: If your database table has a lot of columns, consider adding indexes on the columns that you'll be using in your WHERE clauses and join conditions. This will help the database server to more efficiently process the data.
Batch Inserts: Instead of inserting each row of data one at a time, try to insert multiple rows of data in a single SQL statement. This can be much faster, as the database server only needs to process the SQL statement once, rather than once for each row of data.
Data Types: Make sure that you have chosen appropriate data types for each column in the database table. Using an overly large data type (e.g. using a TEXT field instead of a VARCHAR) can slow down the import process.
Network Bandwidth: If you're importing data from a remote server, consider increasing your network bandwidth to help speed up the import process.
MySQL Configuration: You can make changes to the MySQL configuration file (my.cnf) to optimize the performance of the database server. For example, you can increase the value of the "max_allowed_packet" setting to allow the database server to process larger packets of data at once.
Use LOAD DATA INFILE: The LOAD DATA INFILE statement can be used to efficiently import large amounts of data into a MySQL table. This statement reads data from a text file directly into the database, bypassing the SQL parser and optimizing the import process.

It's worth noting that the most efficient method for importing large datasets into MySQL will depend on the specific details of your dataset and your database setup. You may need to experiment with different methods and configurations to find the one that works best for your particular use case.

However if you are familiar with python then you can also use Google Collaboratory or any other IDE to illustrate at the data or convert it into batches.

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