Saturn cloud
An Alternative to Databricks
When data scientists are choosing a data science platform, they look for several qualities and capabilities that will ensure their work is productive and valuable. They seek a platform that is easy to use, scales with their team, and allows them to use their preferred tools and languages.
Saturn Cloud and Databricks are two popular platforms among data scientists. They both allow data scientists to do their work in the cloud using hosted notebooks, but they differ significantly in their approach and features.
Comparing Saturn Cloud and Databricks
Saturn Cloud provides a data science platform that is more configurable and more interoperable than Databricks.
Platform Features | Saturn Cloud | Databricks |
|---|---|---|
Ease of Use | ||
Approach | Saturn Cloud prioritizes ease of use while still allowing data scientist and teams access to large-scale computing resources. | Databricks prioritizes processing large data first and ease of use second. |
Interactive Environment | ||
Notebooks | Users employ JupyterLab and R, or they can SSH into their preferred IDE, meaning they can replicate their local environment. | Users work in a custom interactive notebook environment, with the limitations inherent to custom solutions. |
Tools & Workflow Flexibility | ||
Libraries | Users can install almost any library or package by utilizing pip, apt, conda, or a start script–minimizing dependency issues and maximizing flexibility. | Users utilize the custom workplace library system to install packages, the complexity of which could lead to dependency and runtime problems. |
Git Repositories | Full git functionality | Limited git functionality (i.e., one branch) |
Scalability & Performance | ||
Instance Types | Saturn Cloud’s upper range is 4 TB and 128 vCPUs, so users can run bigger workloads that require instances of these sizes. | Databricks maxes out at 768 GB of RAM and 96 vCPUs per instance for large-sized instances. |
Distributed Computing | Users can employ Dask clusters, which, for Python users, can not only be easier to debug and control but also be faster. | Users must employ Spark clusters with interface layers to their preferred languages, which can be more difficult to debug and can potentially be slower for some use cases. |
Need a seamless and flexible alternative to Databricks?
Choose Saturn Cloud.
With our open cloud environment, data scientists can work according to their unique business workflows, priorities, and needs, just as if they were working in their local environments. To learn more about the differences between Saturn Cloud and Databricks, check out our comparison whitepaper.