What I value most about AWS Glue is that it allows building and automating ETL processes within the AWS ecosystem without having to manage infrastructure directly. The integration with services like S3, Athena, IAM, Glue Data Catalog, and Step Functions greatly facilitates the orchestration of data pipelines. It is also useful to be able to work with jobs in PySpark and maintain a more scalable architecture for data processing and transformation. In general, it reduces operational effort and speeds up the implementation of pipelines in cloud environments. Review collected by and hosted on G2.com.
What I like the least is that debugging errors is not always straightforward, especially in jobs with PySpark or when configuration issues related to permissions, network, or catalogs arise. The learning curve can also be somewhat steep if you don't have prior experience with data services in AWS. Additionally, some startup and execution times can feel heavy for small loads, and monitoring could be clearer in certain scenarios. Review collected by and hosted on G2.com.
