From the course: Introduction to Modern Data Engineering with Snowflake

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Recap and best practices for data transformations

Recap and best practices for data transformations - Snowflake Tutorial

From the course: Introduction to Modern Data Engineering with Snowflake

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Recap and best practices for data transformations

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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 stored procedures for more complex procedural logic. We took it even further. We saw how streams give fine-grain 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…

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