DataStage ETL migration to PySpark or cloud-native stack
Enterprises are looking to move from DataStage because of the high cost of ownership, complex code, limited documentation, and complex set up process. However, migrating DataStage ETL to a modern data architecture is complex as it is difficult to edit and map columns between the stages. The unavailability of source code versioning also makes it difficult to roll back to previous versions.
Learn how our Automated Workload Transformation Solution addresses all these concerns and automatically converts DataStage ETL to a cloud-native equivalent or PySpark.