Top posts of the year

And their main takeaways
58 implied HN points 19 Jun 23
  1. Building ML pipelines in Snowpark requires using third-party libraries like scikit-learn for machine learning.
  2. Integrating specialized functionalities like graph processing in Snowpark may require additional support or custom solutions.
  3. Adapting a codebase from Apache Spark to Snowpark requires careful consideration and potential restructuring to maintain efficiency and avoid technical debt.
19 implied HN points 29 Jul 23
  1. Performance tuning Snowpark on Snowflake can significantly reduce processing time, from half a day to half an hour.
  2. Utilizing the query profiler by Snowflake and making targeted optimizations can have a high impact on performance.
  3. Optimizations like converting UDTFs to UDFs, caching Dataframes, and using batch size annotations can further optimize Snowpark workflows.