We performed a comparison between AWS Glue and Rivery based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), MuleSoft, Matillion and others in Cloud Data Integration."The most valuable feature for me is the visual interface of AWS Glue."
"The solution integrates well with other AWS products or services."
"I like its integration and ability to handle all data-related tasks."
"The key role for Glue is that it hosts our metadata before rolling out our actual data. This is the major advantage of using this solution and our clients client have been very satisfied with it."
"The solution is stable and reliable."
"AWS Glue is a stable and easy-to-use solution."
"AWS Glue's most valuable features are the data catalog, including crawlers and tables, and Glue Studio, which means you don't have to use custom code."
"The most valuable feature of AWS Glue is that it provides a GUI format with a drag-and-drop feature."
"Connects to many APIs in the market and new ones are being added all the time."
"If there's a cluster-related configuration, we have to make worker notes, which is quite a headache when processing a large amount of data."
"In terms of performance, if they can further optimize the execution time for serverless jobs, it would be a welcome improvement."
"I haven't looked into Glue in terms of seeking out flaws. I've not come across missing features."
"The interface for AWS Glue could improve, they do not put a lot of details. You can write the code, in PySpark or in Scala, which is a big advantage, it is only easy to use for a developer. It will be difficult for new users to enter the cloud environment."
"It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options."
"The mapping area and the use of the data catalog from Glue could be better."
"The product has only a few built-in transformations."
"Currently, it supports only two languages in the background: Python and Scala. From our customization point of view, it would be helpful if it can also support Java in the background."
"Lineage and an impact analysis or logic dependency are lacking."
Earn 20 points
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Rivery is ranked 26th in Cloud Data Integration. AWS Glue is rated 7.8, while Rivery is rated 9.0. The top reviewer of AWS Glue writes "Provides serverless mechanism, easy data transformation and automated infrastructure management". On the other hand, the top reviewer of Rivery writes "Great logic and the ability to call outside API if needed. Key feature is management of different sources". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, Informatica Cloud Data Integration, SSIS and Talend Open Studio, whereas Rivery is most compared with Alteryx Designer and Azure Data Factory.
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We monitor all Cloud Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.