![]() Performing an APPEND on the results to load them into a destination table.Using the results to apply an AWS Glue Studio visual transform.Passing a custom SQL JOIN statement to Amazon Redshift.To demonstrate these new capabilities, we showcase the following: In this post, we explore the new streamlined user interface and dive deeper into how to use these capabilities. ![]() No-code users can complete end-to-end tasks using only the visual interface, SQL users can reuse their existing Amazon Redshift SQL within AWS Glue, and all users can tune their logic with custom actions on the visual editor. With these enhancements, you can use existing transforms and connectors in AWS Glue Studio to quickly create data pipelines for Amazon Redshift. Simplify common data loading operations into Amazon Redshift through new support for INSERT, TRUNCATE, DROP, and MERGE commands.Flexible authoring through native Amazon Redshift SQL support as a source or custom preactions and postactions.Get started faster with Amazon Redshift by directly browsing Amazon Redshift schemas and tables from the AWS Glue Studio visual interface.The new authoring experience gives you the ability to: Today, we are pleased to announce a new and enhanced visual job authoring capabilities for Amazon Redshift ETL and ELT workflows on the AWS Glue Studio visual editor. At AWS re:Invent 2022, we announced support for the new Amazon Redshift integration with Apache Spark available in AWS Glue 4.0, which provides enhanced ETL (extract, transform, and load) and ELT capabilities with improved performance. AWS Glue provides an extensible architecture that enables users with different data processing use cases, and works well with Amazon Redshift. This is commonly achieved via AWS Glue, which is a serverless, scalable data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources. One of the most common use cases for data preparation on Amazon Redshift is to ingest and transform data from different data stores into an Amazon Redshift data warehouse. In particular, we have observed an increasing number of customers who combine and integrate their data into an Amazon Redshift data warehouse to analyze huge data at scale and run complex queries to achieve their business goals. In a modern data architecture, unified analytics enable you to access the data you need, whether it’s stored in a data lake or a data warehouse.
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