6 Powerful Scalable Computing Platforms 2022 Edition
As data volumes grow, the demand for scalable computing tools does as well. Fortunately, the open source community has responded with a plethora of new tools to parallelize code, accelerate computation with GPUs, and deliver faster time-to-value for teams with big data.
While some teams have the DevOps resources and budget to create the infrastructure to host open source tools securely, others simply do not have time, budget or resources. We have compiled a list of the top scalable compute platforms that provide top hosted solutions that work securely with enterprise data.
1. Saturn Cloud
Without having to switch any tools, Saturn provides a flexible environment where data scientists can launch high-powered notebooks (Jupyter, R, VS Code, and more) in the cloud, quickly use Dask and Bodo clusters, GPUs, deploy cloud resources to expand their data science capabilities, collaborate throughout an entire project lifecycle, and more.
Saturn Cloud offers a free community tier as well as enterprise tiers that install directly in the AWS virtual private cloud.
Anyscale is a fully-managed Ray offering, from the creators of Ray. It accelerates building, scaling and deploying AI applications on Ray by eliminating the need to build and manage complex infrastructure.
Bodo is the platform to take your Python and SQL data analytics code directly to production with extreme performance and massive scaling through automatic under-the-hood parallelization.
Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production.
Databricks is a Unified Analytics Platform on top of Apache Spark that accelerates innovation by unifying data science, engineering and business. With our fully managed Spark clusters in the cloud, you can easily provision clusters with just a few clicks.
Building enterprise-ready tools for rapid, flexible experimentation with data at scale. Operate on data at any scale, while continuing to use the familiar Pandas API. Powered by open-source Modin and Lux.
Data science without memory limits is considered the future by leaders in the space. The solutions mentioned above offer some of the most effective and promising tools for those looking to scale computation, without incurring the pain of DevOps and costly budget impact. Feel free to share your contributions to firstname.lastname@example.org to help us continuously improve this list.