Published on Nov 15, 2022
Snowflake announced exciting new additions to its platform at Snowday 2022. Snowflake’s single data platform will enable developers, data scientists, and engineers to increase productivity and discover new ways to develop applications, pipelines, and machine learning models.
Interestingly, Snowflake has announced that users will be able to develop data applications using Python directly on its platform after announcing its acquisition of Streamlit, an open-source framework for machine learning and data science teams. Applications created can then be run on Snowflake’s secure and governed platform.
Snowflake’s developer framework, Snowpark, supports a number of programming languages, including Java, Scala, and SQL. With Python, developers are able to co-create projects without having to worry about data security or compliance issues.
Additionally, Snowflake has been able to leverage the capabilities of its partners, including Anaconda, dbt labs, and others. As a result of Anaconda’s integration with Snowflake, the open-source Python library in Anaconda will now be available to Snowflake users. As a result of the merger, manual installation and package dependency management are no longer required. Snowflake’s integration with DBT Labs, on the other hand, brings together the power of SQL and Python, bridging the widening gap between analytics and data science.
Additionally, it is also planning to release its own optimized warehouses, which can be publicly previewed in AWS, so developers can run large scale machine learning training and other memory-incentive operations in Snowflake, as well as Python Worksheets for private view, where applications, data pipelines and machine learning models can be developed.
Snowflake has also taken other measures to enable developers to build applications in the Data Cloud. The Schema Interface enables developers to onboard data more quickly, thus increasing productivity, while also executing pipelines seamlessly with Serverless Tasks natively within the platform. Additionally, Snowflake has introduced two new tools: dynamic tables and observability & experiences.
In order to facilitate coding efficiency and ease, dynamic tables automate incremental processing through the use of declarative data pipelines. To build, test, debug, deploy, and monitor data pipelines more efficiently, observability & experiences include alerting (private preview), logging (private preview), event tracing (private preview), task graphs, and history (public preview), among others.
Python is known to be the most popular programming language among data scientists and the third most popular among developers. With Python support and an open-source library integrated into Snowflake, the company will be able to attract a large portion of the developer community.
