AWS Glue simplifies ETL with its serverless architecture and tight integration with the AWS ecosystem. The built-in Data Catalog, schema discovery, and job automation features reduce operational overhead. It is highly scalable, cost-effective, and suitable for both batch and streaming workloads. Review collected by and hosted on G2.com.

One downside of AWS Glue is the job startup time, which can be slow for small workloads. Debugging Spark jobs is sometimes difficult, especially when logs are not very clear. Cost can increase if jobs are not optimized properly. Also, the UI can feel limited for complex transformations compared to full-scale Spark development Review collected by and hosted on G2.com.

The best thing I like about AWS Glue is that it provides serverless ETL capabilities without having to manage infrastructure. AWS Glue also supports schema enforcement through the Glue Data Catalog. It’s easy to integrate with S3 and Athena seamlessly. It also supports PySpark for big data processing, which is another reason I like AWS Glue. Review collected by and hosted on G2.com.

I don’t have any major dislikes about AWS Glue. Overall, it’s a good tool and has worked well for what I need. Review collected by and hosted on G2.com.

Serverless service.

Scalable (based on number of workers).

Easy setup. Can start with simple Python script.

Abstracts away some of the complexities of PySpark. Review collected by and hosted on G2.com.

Single script file based - It is difficult to deploy modular code on Glue. There are options to zip and link. But the UI only displays entrypoint file. could have been much better if it was similar to lambda functions in terms of deployment and packaging.

Auto scaling sometimes doesn't work as expected. Unexpected compute usage even when running single threaded low compute tasks.

No direct integration with Event bridge.

Logging is problematic. By default, all jobs uses one log group. Difficult to parse logs. Review collected by and hosted on G2.com.

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.

I appreciate AWS Glue's serverless model and data catalog because it makes it easy to process large datasets and maintain metadata without managing clusters manually. Its serverless architecture simplifies building ETL workflows without worrying about infrastructure, which is fantastic for data integration, especially when working within the AWS ecosystem. The crawler and catalog features are particularly useful as they automate schema discovery and metadata management. Schema inference and automated data discovery through crawlers significantly reduce manual setup and simplify onboarding new datasets. These features help in managing metadata efficiently and automatically infer new data schemas when data gets loaded to our storage bucket. Review collected by and hosted on G2.com.

Debugging complex jobs can sometimes be difficult. Especially when working with large part-based transformations, job startup time can also add latency and troubleshooting failures occasionally requires checking multiple logs and configurations. Review collected by and hosted on G2.com.

The catalog and the crawling services that Glue has make it very easy to design both full and incremental loads. it is very easy and everything can be done from the AWS Glue UI screen itself, thats the best part. Review collected by and hosted on G2.com.

We have millions of databases and millions of tables in each database, and it’s very difficult to search and get to the table we’re looking for because Glue only shows the first 100 records. As a result, this takes a lot of time, and consumers have to wait until it’s done. Review collected by and hosted on G2.com.

What I like most about AWS Glue is that it is a fully managed ETL service, so there is very little infrastructure to worry about. It integrates seamlessly with other AWS services like S3, Athena, Redshift, and Lake Formation, which makes data movement and transformation very smooth. The Glue Data Catalog is especially useful because it automatically discovers and organizes data, saving a lot of manual effort. The serverless nature and auto-scaling also help optimize costs since we only pay for what we use. Review collected by and hosted on G2.com.

One challenge with AWS Glue is the learning curve, especially for beginners who are new to Spark or AWS services. Debugging Glue jobs can sometimes be difficult because the error messages are not always very clear. Job startup time can also feel a bit slow for smaller workloads. Additionally, costs can increase quickly if jobs are not optimized properly, so monitoring and tuning are important. Review collected by and hosted on G2.com.

It simplifies discovering, preparing, and combining data for analytics, machine learning, and application development and provides features like data quality, schema management . Review collected by and hosted on G2.com.

It gets start-up times, complex debugging processes. sometimes taking several minutes to start. Review collected by and hosted on G2.com.

Easy to trace the error in the script through runs.

My team build a framework to fetch data from different platform through AWS Glue and stores them in S3 in the file format mention by us. That make our integration and fetching data a lot easier.

Sppourt frequent data load.

It is easy to implement.

Good customer support. Review collected by and hosted on G2.com.

Many a time I face server/connectivity issue when using glue and our job get blocked due to that.

Does not support xml file formats. Review collected by and hosted on G2.com.

Absolutly love Glue for its functionality. Provides everything needed Review collected by and hosted on G2.com.

The legacy version is now not accesible. Also, the documenttation could be beeter. Review collected by and hosted on G2.com.