An alternative solution is to use Google BigQuery, which is a fully-managed, serverless data warehouse by Google. You can import your large dataset into BigQuery and perform the necessary transformations and calculations directly in the BigQuery environment. Once the processing is complete, you can export the results back to Google Sheets if needed.

One possible approach is to split the dataset into smaller chunks and process each chunk separately. You can use scripts to automate this process and stitch the data back together once the processing is complete. Another option is to utilize Google Apps Script's Advanced Google Services, such as the Google Sheets API, which allows you to work with larger datasets more efficiently by leveraging resources outside of the Google Sheets environment.

One interesting technique to handle large datasets in Google Sheets is to use the FILTER function along with the TRANSPOSE function. By using these functions together, you can dynamically extract subsets of data from your large dataset and display them in a more manageable way. This allows you to work with smaller portions of the data at a time, reducing the risk of crashing the program or exceeding the memory limit.