AWS – Amazon SageMaker offers additional visual ETL transforms and S3 tables support

Amazon SageMaker now offers 14 new built-in Visual ETL transforms: “Format timestamp”, “Split string”, “Regex extractor”, “Autobalance processing”, “UUID (Universally Unique Identified)”, “Identifier”, “Unpivot columns into rows”, “Pivot rows into columns”, “Parse JSON column”, “Extract JSON path”, “Lookup”, “Router”, “Select from collection” and “Order By”. With these transforms, ETL developers can quickly build more sophisticated data pipelines without having to write custom code for common transform tasks. Also, Amazon S3 Tables are now supported via the Amazon SageMaker Lakehouse node. Providing you with the flexibility to access and preview data in-place across S3 Tables.

Visual ETL in Amazon SageMaker provides a drag-and-drop interface for building ETL flows and authoring flows with Amazon Q Developer. Each of the new visual ETL transforms address a unique data processing need. For example, use “Identifier” to assign a numeric identifier for each row in the dataset, transform JSON strings with “Parse JSON column” which allows you to covert a JSON string into a data struct or array, or extract just the JSON path you need with “Extract JSON path” transform.

These Visual ETL transforms are now available in all AWS regions where Amazon SageMaker is available. Access the supported region list for the most up-to-date availability information.

To learn more, visit our Amazon SageMaker documentation.
 

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