Recap and best practices for data transformations
1. Recap and best practices for data transformations
We covered a lot of ground in this data transformations module and did a lot of leveling up. Let's quickly recap what you learned. We covered the core languages and libraries for performing data transformations, SQL and Snowpark. We specifically covered Snowpark for Python, but you also learned that you can write Java and Scala with Snowpark as well. We went from using these core languages to using objects that make capturing and reusing logic in our transformations easy, user-defined functions for things like calculations, and store procedures for more complex procedural logic. We took it even further. We saw how streams give fine-grained control over changes to an underlying table and how they can be used for incremental data processing. And we also saw how to accelerate transformations with dynamic tables, which allow you to set the desired end state of a table by associating transformation logic and a refresh rate for the transformations. And last but not least, you learned how to write all of this code in either Snowflake Worksheets, Snowflake Notebooks, or Snowflake's VS Code extension. All of these things will make you dangerously good at performing transformations against raw data, which means that you're that much closer to building pipelines that not only ingest and transform, but also deliver value through a data product. So how exactly can you go about delivering this value? Join me in the next module to learn more about data delivery with Snowflake.2. Let's practice!
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