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AWS Glue is a completely overseen ETL (extract, transform, and load) service that makes it straightforward and cost-effective to arrange your data, clean it, improve it, and move it reliably between different data stores and data streams. AWS Glue comprises a central metadata storehouse known as the AWS Glue Data Catalog, an ETL engine that automatically creates Python or Scala code, and an adaptable scheduler that handles dependency resolution, job monitoring, and retries. AWS Glue is serverless, so there's no framework to set up or manage.

AWS Glue is intended to work with semi-organized data. It presents a component called a dynamic frame, which you can use in your ETL scripts. A dynamic frame is like an Apache Spark data frame, which is a data abstraction used to sort out data into rows and columns, then again, except that each record is self-defining so no schema is required in the starting. With dynamic frames, you get schema flexibility and a set of advanced transformations explicitly intended for dynamic frames. You can change over between dynamic frames and Spark data frames, with the goal so that you can take advantage of both AWS Glue and Spark transformations to do the sorts of reviews that you need.

Benefits of AWS Glue:

• Less hassle
• More Power
• Cost-effective

For more details, read the full blog:

All About AWS Glue