We performed a comparison between AWS Glue and Informatica Enterprise Data Catalog based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We no longer had to worry much about infrastructure management because AWS Glue is serverless, and Amazon takes care of the underlying infrastructure."
"The solution is stable and reliable."
"It is a stable and scalable solution."
"It's fairly straightforward as a product; it's not very complicated."
"AWS Glue is a good solution for developers, they have the ability to write code in different languages and other software."
"Glue is a NoSQL-based data ETL tool that has some advantages over IIS and ISAs."
"We have found it beneficial when moving data from one source to another."
"AWS Glue's best features are scalability and cloud-based features."
"I rate the technical support a ten out of ten."
"The solution scales well."
"We can scan anything."
"The product seems stable enough."
"The most valuable feature is its ability to extract metadata from various sources- be it an old SaaS application or the latest cloud application."
"The way that the solution scans is very useful."
"The capability of the tool to scan and capture the metadata from a variety of sources is one of the capabilities that I find most useful. The central repository into which it is going to put that captured metadata is the best."
"I like EDC's self-service capabilities. You can put the catalog on the intranet inside the organization, so users can search for something. People in the research world have specialized systems, and you might find data from various places that sound similar."
"The solution could be cheaper. The price of the solution is an area that needs improvement."
"The technical support for this solution could be improved. In future, we would like to connect more services like Athena or Kinesis to help control more loads of data."
"On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded."
"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."
"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."
"It fails to handle massive databases acquired from various sources."
"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."
"They have to improve their relationship discovery tool. They say that they have AI inside, but this AI did not automatically find relationships or suggested relationships between entities."
"IEDC can improve the comparison of lineages."
"This solution is hard to set up and its interface is not user-friendly. It's also not as stable, and the technical support takes a lot of time to solve simple problems."
"The UX and UI of the solution are areas with certain shortcomings where improvements can be made in the future."
"It is more complicated to extract data using the product compared to Visio. The system could display the details on the screen."
"It is not easy to set up and configure the tool."
"The solution is quite expensive."
"Informatica Enterprise Data Catalog could improve by having a much better user interface. It is not user-friendly."
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AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Informatica Enterprise Data Catalog is ranked 1st in Metadata Management with 13 reviews. AWS Glue is rated 7.8, while Informatica Enterprise Data Catalog is rated 7.6. 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 Informatica Enterprise Data Catalog writes "Great metadata management with more visibility and great technical support". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, SSIS, Informatica Cloud Data Integration and Denodo, whereas Informatica Enterprise Data Catalog is most compared with Alation Data Catalog, Collibra Catalog, Informatica PowerCenter, Denodo and Palantir Foundry. See our AWS Glue vs. Informatica Enterprise Data Catalog report.
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