We performed a comparison between AWS Glue and Informatica Enterprise Data Lake based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon, MuleSoft, Matillion and others in Cloud Data Integration."I appreciate AWS Glue for its cost-effectiveness."
"The most valuable feature of AWS Glue is its ease of use and good documentation. Additionally, we can do all the transformations that we need."
"AWS Glue is quite better than other tools, but you have to learn it properly before you start using it."
"I like the fact that AWS Glue works with Python scripts."
"What I like best about AWS Glue is its real-time data backup feature. Last week, there was a production push, and what used to take almost ten days to send out around fifty-six thousand emails now takes only two hours."
"The most valuable feature of AWS Glue is that it provides a GUI format with a drag-and-drop feature."
"The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features."
"The most valuable feature for me is the visual interface of AWS Glue."
"The process of using the tool's scalability option is well documented."
"On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded."
"Glue could perform better. It sometimes takes too long to test a Glue job. Google Cloud Platform offers more Python scripts than AWS."
"The price of the solution could improve."
"I have encountered challenges with multi-region support."
"There should be more connectors for different databases."
"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."
"I would like to see stable libraries at the moment they are not there."
"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."
"Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems."
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AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Informatica Enterprise Data Lake is ranked 39th in Data Integration with 1 review. AWS Glue is rated 7.8, while Informatica Enterprise Data Lake is rated 7.0. 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 Informatica Enterprise Data Lake writes "A scalable tool that needs a lot of maintenance due to its unstable nature". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, SSIS, Informatica Cloud Data Integration and Talend Open Studio, whereas Informatica Enterprise Data Lake is most compared with Palantir Foundry, Oracle Data Integrator (ODI), IBM InfoSphere DataStage and Azure Data Factory.
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