Snowpark

Native developer framework within Snowflake, the leading cloud data platform, competing with Databricks and BigQuery ML for in-warehouse ML and pipeline workloads

Snowpark is Snowflake's developer framework that enables data engineers, data scientists, and developers to write code in their preferred programming languages—Python, Java, or Scala—and execute it directly within Snowflake's Data Cloud without moving data. By pushing computation to where the data lives, Snowpark eliminates the need for complex ETL pipelines and reduces data movement overhead, enabling faster and more secure ML model training, feature engineering, and data transformation workflows. Snowpark has become a central pillar of Snowflake's platform strategy, allowing organizations to build end-to-end machine learning pipelines, deploy user-defined functions (UDFs), and create scalable data applications natively within Snowflake. The acquisition of Streamlit in March 2022 for approximately $800 million extended Snowpark's capabilities to include interactive data application development, enabling teams to build and share data-driven apps directly on top of Snowflake data without additional infrastructure. In the AdTech ecosystem, Snowpark is increasingly relevant as advertisers, agencies, and data clean rooms leverage Snowflake for audience segmentation, attribution modeling, and identity resolution. Snowpark enables these workflows to be operationalized at scale within a governed, secure environment. It competes broadly with Databricks' collaborative notebooks and MLflow ecosystem, Google BigQuery ML, and other in-warehouse compute frameworks, positioning Snowflake as a full-stack data and AI platform rather than just a cloud data warehouse.