Data Pipelines with Burst
Run ETL jobs and data transformations without provisioning infrastructure. Process data in the background while you focus on building.
The Problem
Data pipelines are the backbone of modern applications, but running them is a pain:
- Airflow/Dagster require servers and orchestration setup
- Cloud ETL services are expensive and complex
- Local execution ties up your machine for hours
- Cron jobs on VPS need constant monitoring
You just want to run a Python script that processes data. Why does it need to be so complicated?
How Burst Solves It
With Burst, running a data pipeline is as simple as running a local script. Your code executes in an isolated VM, and you get the results when it's done.
$
burst run ./pipeline.py
Uploading... done.
Running in vm-9m2k.
You can close this terminal.
Check progress or wait for completion:
$
burst logs
[09:15:23] Extracting from source...
[09:17:45] Transforming 1.2M records...
[09:22:11] Loading to destination...
Done. 8m 42s
Why Burst for Pipelines
- No orchestration — Just run your script
- Full Python environment — Install any packages you need
- Long-running support — Process for hours without timeouts
- Isolated execution — Each job gets its own VM
- Simple monitoring — Stream logs in real-time
Comparison
| Feature | Burst | Airflow | Lambda |
|---|---|---|---|
| Setup time | 1 min | Hours | 30+ min |
| Max runtime | Unlimited | Unlimited | 15 min |
| Learning curve | None | DAGs, operators | AWS services |
| Infrastructure | None | Servers | AWS config |
Ready to simplify your data pipelines?
Get early access