AWS Glue

AWS Glue is well-suited for data warehousing scenarios where you need to extract, transform, and load data into a centralized repository like Amazon Redshift. It simplifies the ETL.It's a great choice for preparing data in data lakes, especially when dealing with diverse data sources and formats. Glue can help normalize and structure data for analytics.

ETL operations and jobs are well suited to perform with glue. If we want to transform or extract data from the data sources specially in the data stored in the AWS cloud . It is very well integrated with the other AWS services. It is easier to establish connections. We can schedule the crawlers or run on demand.

well suited:- when you want to use it to transform your data then glue also provides there own transformation also in that option you can able to do PII masking of Data if you don't want to use any code approach. The second scenario would be when want to integrate glue with other AWS services. and also wants to run Spark on glue for faster processing. less appropriate:- If you want to integrate with other services which are outside of AWS. it does not support Java as of now so if you have java resources then you can not run it.

AWS Glue is a mature product, which helps organizations start their journey with data exploration and analysis. AWS Glue has many great features, like a data catalog, jobs, crawlers, helping non-engineers to handle data and build a data lake.

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).

AWS glue is best if your organization is dealing with large and sensitive data like medical record. Its comes with scheduler and easy deployment for AWS user. The data catalog keeps the reference of the data in a well-structured format. If you are already part of the AWS services, then AWS Glue is the best choice; otherwise, it's not a simple one for deployment.