AWS Glue

I like AWS Glue because it is fully serverless. ELT service that eliminates the need to manage infrastructure while automatically scaling with data volume. It integrates seamlessly with AWS services such as S3, Athena, Redshift, and Snap Lake Formation and Glue, simplifies data transformation using Pyspark and spark SQL and its data catalog and crawlers make schema discovery and metadata management easy.

I like the serverless model, which removes the need to manage infrastructure. It scales well for large data jobs. integration with AWS data stack is strong. The data catalog is also useful for managing schemas and metadata.

Glue is quite robust and has a correct user interface. It connects out of the box to other ETL AWS Services and allows good monitoring of ETL processes. The error handling is also mostly correct.

AWS glue can be difficult to debug due to verbose spark logs and limited error visibility. Job startup times are slow because of cold starts, managing dependencies and spark versions can be complex and cost may inefficient DPU allocations. It is not suited for low-latency or real-time processing use cases.

Job debugging is difficult when failure occurs, logs are not always clear. Costs can rise without clear signals.

The interface seems outdated (which is usual in this type of vendor products, but still). Error catching may take minutes because of so many menus that I should open before seeing what's wrong. It's also not a very visual tool, with only devs who know programming languages going into to see the errors - other popular tools might propose a better visual interface to check where the problem occurred.