Snowflake has revealed new enhancements designed to make it easier for developers to build apps on its data cloud platform while reducing reliance on third-party tools.
Following its acquisition of Streamlit in March 2022, an open source app framework in the Python language, the company is now integrating the framework with its own technology. It said this will help developers bring their data and machine learning (ML) models to life as secure and interactive applications on Snowflake.
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The cloud data company hopes the Streamlit integration will help developers build powerful applications without the complexity of building and deploying web applications. The integration will help developers create applications with Python using their data in Snowflake, and then deploy and run these applications on Snowflake’s platform before sharing them with business teams.
“As we continue to disrupt application development, we’re giving builders the data access and tools they need to accelerate their pace of innovation securely under Snowflake’s one unified platform,” said Torsten Grabs, director of Product Management at Snowflake.
“Snowflake’s advancements provide developers with the capabilities to build powerful applications, pipelines, and models with the utmost confidence, and eliminate complexity so they can drive value across their organisations with the Data Cloud.”
To further reduce the need for developers to turn to third-party tools, Snowflake is also providing access to Python and its open source libraries to all users and teams, with the general availability of Snowpark for Python. This means developers will be able to use Python to query and process data queries in a data pipeline while remaining on the company’s platform.
Snowflake said that since its public preview announcement in June 2022, Snowpark for Python has seen six times growth in adoption, with hundreds of customers building their data using the platform, including EDF, Sophos, and Northern Trust.
Through Snowpark, developers also get a streamlined architecture that natively supports users’ programming languages of choice, including Java, Scala, SQL, and Python. Snowflake is also releasing Snowpark-optimised warehouses, which are currently in public preview in AWS, so Python developers can run large scale ML training and other memory-intensive procedures directly in Snowflake. They can also use Python Worksheets, which is in a private preview, to develop applications, data pipelines, and ML models inside Snowflake.
Lastly, Snowflake is also making it easier to work with streaming data within a single platform and further eliminate silos for customers. Users are able to boost productivity by onboarding faster with Schema Interface, and execute data pipelines with Serverless Tasks on Snowflake’s platform.
