Scalability with Saturn Cloud
The Dask advantage brings distributed parallel computing in Python for data teams
Traditional methods for scaling and deploying Python involve multiple tools, multiple teams, and multiple programming languages. Saturn solves this by integrating the resources and frameworks for scalable Python as a managed service, so teams can focus on data science, and offload everything else
Distributed Parallel Computing in Python
Python-Native, Light Weight
- ∙ Increases algo speed by 10-100x
- ∙ Highly preferred by Python users (~80% data scientists)
- ∙ Shortens time to value with greater agility (build, deploy, debug in Python)
Support For Multi-Dimensional Arrays
- ∙ Required for geospatial analysis and other big data applications
- ∙ Example: x-coordinate, y-coordinate, time
- ∙ Not supported by existing frameworks like Spark
GPU Acceleration (Led by Nvidia)
- ∙ Faster hardware performance VS. traditional CPU processing
- ∙ Compatible with strategic Nvidia initiatives including RAPIDS
- ∙ The future of ML and DL
Use Case:
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