File Sharing for Data Scientists

You trained a model overnight. The results look promising and your collaborator needs the output files, the notebook and the dataset you used. Email cannot handle a 2 GB Parquet file. Cloud drives want everyone on the same platform.

The Data Sharing Bottleneck

Data science workflows produce large files. CSV exports from database queries, Parquet files from Spark pipelines, Jupyter notebooks with embedded visualizations and trained model weights that run into gigabytes. Getting these files from your machine to a colleague should not require setting up an S3 bucket or fighting with institutional IT policies.

Most sharing tools fall short. GitHub has a 100 MB file limit. Email tops out at 25 MB. Google Drive wants both parties logged in. You end up compressing files, splitting archives or writing upload scripts just to hand someone a dataset.

How to Share Data Science Files with EasySend

1. Upload your files - drag datasets, notebooks and model outputs onto easysend.co
2. Bundle related files - group the notebook, dataset and requirements.txt together in one upload
3. Share the link - send the URL through Slack, email or your project management tool
4. Automate with the API - use the developer API to upload files programmatically from your pipeline scripts

Features That Fit Data Workflows

• Large file support - upload datasets up to 5 GB without compression or splitting
• No recipient accounts - collaborators click the link and download. No platform lock-in.
• API access - integrate file sharing into your Python scripts and CI/CD pipelines with the REST API
• Download tracking - confirm that your collaborator actually grabbed the files
• Password protection - gate access to proprietary datasets and unpublished results

Common Data Science Sharing Scenarios

Share a cleaned dataset with a teammate who needs to reproduce your analysis. Send model checkpoints to a collaborator running experiments on a different machine. Deliver final results to a stakeholder who just needs the output CSV and a summary notebook. Upload training logs and evaluation metrics for a project review meeting.

The API is especially useful for automated pipelines. Add a few lines of code to your training script and it uploads the output files when the job finishes. Your team gets a fresh link every run without manual intervention.