Guidance Needed for Importing Large Datasets into Microsoft Fabric Notebooks

Hi js15,

Thank you for reaching out to Microsoft Fabric Froum Community. 

Regarding importing large datasets into Microsoft Fabric Notebooks. To handle such datasets efficiently, please follow the below steps: 

To prevent memory issues and ensure efficient processing, consider breaking down the dataset into smaller chunks. Many tools, such as pandas, support reading data in chunks, allowing only manageable portions to be loaded into memory at a time. 

• CSV Files: Use the chunk size parameter in pandas to read the dataset in segments. This helps minimize memory usage by working with smaller portions of the data. 
• Parquet Files: For Parquet files, utilize libraries like pandas,which support chunked reading and are optimized for large datasets, ensuring better memory management. 

Upon processing each chunk, you have the option to handle them separately or merge them into a single DataFrame, based on your analytical needs. This can be achieved using pandas concat function or other appropriate methods. 

If the dataset is very large, parallel processing can expedite the import and processing phases.

When handling large datasets, it is crucial to monitor memory usage. Adjust the chunk size based on your available memory to avoid overflow.

If this post helps, then please give us ‘Kudos’ and consider Accept it as a solution to help the other members find it more quickly. 

Thank you.