Snowflake’s updates include the introduction of support for Python on Snowpark, data access capabilities, and external tables for on-premises storage.
Cloud-based data warehouse company Snowflake on Tuesday at its annual Snowflake Summit introduced a new set of tools and integrations to take on rival firms such as Teradata, and services such as Google BigQuery, and Amazon Redshift. The new capabilities, which include data access tools and support for Python on the company’s Snowpark application development system, are aimed at data scientists, data engineers and developers with the intent of accelerating their machine learning journey, in turn speeding up application development. Snowpark, launched a year ago, is a dataframe-style development environment designed to allow developers to deploy their preferred tools in a serverless manner to Snowflake’s virtual warehouse compute engine. Support for Python is in public preview.
« Python is probably the single most requested capability that we hear from our customers », said Christian Kleinerman, senior vice president of products at Snowflake. The demand for Python makes sense, as it is a language of choice for data scientists, analysts say. In one of the updates announced at the summit, the company said that it was adding a Streamlit integration for application development and iteration. Streamlit, which is an open source app framework in Python targeted at machine learning and data science engineering teams to help visualize, change and share data, was acquired by Snowflake in March. The integration will allow users to stay within the Snowflake environment, not only to access, secure, and govern data, but to develop data science apps to model and analyze data, said Tony Baer, principal analyst at dbInsights. Some of the other Python-related integrations include Snowflake Worksheets for Python, Large Memory Warehouses, and SQL Machine Learning.
