Best Practices for Python Data Transformations in Snowflake

Data preparation can be a headache with legacy architecture that requires manual environment configuration, job tuning, and cluster management. Snowpark, Snowflake’s developer platform, changes that by making it easier than ever for data engineers and data scientists to transform and process data. Snowpark natively supports SQL, Python, Java, and Scala on a single platform to enable data wrangling for pipelines feeding ML models, analytics, and applications faster and more securely with Snowflake’s elastic processing engine. Watch this webinar with Snowpark experts on Python best practices to: - Execute custom Python code with stored procedures and various UDF types for ELT/ETL - Seamlessly access popular open source packages through the built-in Anaconda integration - Use Snowpark-optimized warehouses for memory-intensive operations on large datasets - Implement Sklearn-style transformers with Snowpark to take advantage of Snowflake’s parallelization to scale feature engineering