Data science startup Saturn Cloud launches Dask, a parallel computing technology to accelerate big data analytics

Saturn Cloud, a provider of data science tools for the enterprise, today announced it has launched the first-ever commercial offering of Dask, a Python-native parallel computing framework for scalable data science. The announcement comes just one month after the startup closed $4 million in seed funding round.

Founded just this year by Hugo Shi, and M. Sebastian Metti, the New York-based Saturn Cloud is a premier data science platform for enterprise. Its platform gives data scientists the tools to do all the things they want: seamless collaboration, effortlessly scalable compute resources, and easy analytics. Saturn Cloud helps data science teams to operate quicker independent of devops and data engineering teams.

Dask offers data scientists advanced parallelism for analytics. Using existing Python APIs and data structures, Dask makes it easy to switch between Numpy, Pandas, and Scikit-learn to their Dask-powered equivalents. This makes Dask a natural choice for data scientists looking to scale analytics as it does not require knowledge of Java or Scala.

“We are huge fans of Dask because it enables our team to get answers from big data in minutes instead of weeks,” says Director of Data Science at a Fortune 1000 healthcare company. Previously, most data scientists in industry would wait weeks for their code to compile while analyzing large datasets; with Dask’s parallelism, the code can execute at a speed that’s not possible with standard computing equipment.

As part of broadly introducing Dask to industry, Saturn is filling enterprise adoption needs around vendored support and service-level agreements, which has previously slowed Dask adoption at the enterprise level. The company is also offering a SQL compiler for businesses to more easily integrate Dask into their existing processes.

“The massive amounts of data being created is making companies look for new innovative ways to handle their information — parallel computing offers a low-cost way to solve this problem instead of buying more powerful computers for data scientists,” says Demi Ajayi, engineer, employed at Fortune 50 aerospace and technology company.

“We invested in Saturn because they saw the converging trends of big data growth, Python takeover in data science, and rise of cloud computing — creating a once-in-a-lifetime opportunity to offer industry an end-to-end data science platform equipped with automation and big data processing tools,” recounts Ilya Kirnos, founder of SignalFire.