![]() Snowflake can scale your ETL workload across hundreds or even thousands of nodes.If your ETL job takes 2 minutes to run you only pay for those 2 minutes. Snowflake follows a utility billing model.Benefits of using Snowflake for data engineering There couldn’t be a better fit than Snowflake for the job. With Snowflake you can create an external table over your data on S3, apply the transformations for feature engineering and then unload the data back to S3 in Parquet for downstream processing by your data science platform. Let’s say you want to build the features for a predictive model. The raw data on your data lake requires transformations. Your company has built a data lake on object storage, e.g. ![]() Let’s take this common scenario as an example: In this blog post we will give you some hands-on advice on how you can use Snowflake as an ETL engine for data preparation. Did you know that the Snowflake data platform is the perfect fit for ETL, data integration, and data preparation tasks?
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