Saturn Cloud, a provider of data science tools, announced it has launched the first-ever commercial offering of Dask, a Python-native parallel computing framework for scalable data science. This comes one month after the startup’s first round of funding was announced.
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 companies.
“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.