We performed a comparison between AWS Glue and Informatica Cloud Data Integration based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: For users vested in the AWS ecosystem, AWS is hands down the best choice. Informatica Cloud Data Integration is flexible and allows users to decide how to distribute their IPUs in their own networks. Data residency laws make it challenging to choose this solution, as their regions are currently very limited.
"The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features."
"AWS Glue is a good solution for developers, they have the ability to write code in different languages and other software."
"Its user interface is quite good. You just need to choose some options to create a job in AWS Glue. The code-generation feature is also useful. If you don't want to customize it and simply want to read a file and store the data in the database, it can generate the code for you."
"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."
"I like that it's flexible, powerful, and allows you to write your own queries and scripts to get the needed transformations."
"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."
"Glue is a NoSQL-based data ETL tool that has some advantages over IIS and ISAs."
"It is AWS-integrated. There is end-to-end integration with the other AWS services. It is also user-friendly."
"It has all the advantages of the Cloud in that you can use it without worrying about infrastructure, upkeep, or upgrades."
"The product is scalable and excellent for enterprise-level organizations."
"Their new licensing is very flexible. With Informatica Cloud, you have plenty of items under the same umbrella, such as services, offerings, data quality, and data masking. You have also got master data management and API management. What I really like about them is that you don't need to go to Informatica and say that you need a data integration module. You would say that you need iPaaS or Informatica Cloud. They'll then try to understand your needs and give you IPUs, which are the processing units. If I purchased a hundred IPUs from Informatica as a customer, I can use 70 IPUs for data integration. I would also need data quality, so I can use 10 IPUs for data quality. I can use the remaining 20 IPUs for API management. Down the line, if I see that my initial data integration needs for the development phase are met, then out of the 70 IPUs assigned for data integration, I can use 30 IPUs for data masking. I can shuffle these numbers in any way within the Informatica Cloud umbrella for the tenure for which I have subscribed to these IPUs. I can use all services the way I want. This flexibility is what I really love about Informatica. It also has got good connectors."
"The solution's initial setup is quite straightforward."
"Informatica Cloud Data Integration is stable."
"The mass ingestion functionality and the elasticity of the solution are great."
"We have a lot of integrations, and it's very easy to create integrations. They have a lot of connectors."
"Informatica Cloud Data Integration is stable."
"The monitoring is not that good."
"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."
"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."
"Overall, I consider the technical support to be fine, although the response time could be faster in certain cases."
"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."
"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."
"There is a learning curve to this tool."
"There should be more connectors for different databases."
"I have received feedback from certain teams and there is a steep learning curve to use this solution."
"The price modeling could be more flexible."
"Informatica Cloud Data Integration could improve the price by making it less expensive."
"The cloud version of the Informatica, it's a very substandard product. They might say it's enterprise-ready but it's not at all ready. They need to add more features, such as improved data replication features. If you look at other tools, such as Matillion they are now cloud-native and flexible. Additionally, Informatica Cloud Data Integration should have a good migration strategy from Informatica PowerCenter to Informatica Cloud Data Integration."
"Cost-wise, it could be better."
"Its pricing model can be improved. The response time from technical support can also be improved."
"The current features are a bit complicated, and we need to write big scripts and test."
"There may be some types of limitations with the performance."
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL.
AWS Glue is ranked 2nd in Cloud Data Integration with 10 reviews while Informatica Cloud Data Integration is ranked 3rd in Cloud Data Integration with 10 reviews. AWS Glue is rated 8.2, while Informatica Cloud Data Integration is rated 8.0. The top reviewer of AWS Glue writes "Easy to perform ETL on multiple data sources, and easy to use after you learn it". On the other hand, the top reviewer of Informatica Cloud Data Integration writes "A UI-based tool with great scripting ". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, Talend Open Studio, SSIS and IBM InfoSphere DataStage, whereas Informatica Cloud Data Integration is most compared with Informatica PowerCenter, Azure Data Factory, Mule Anypoint Platform, SSIS and Matillion ETL. See our AWS Glue vs. Informatica Cloud Data Integration report.
See our list of best Cloud Data Integration vendors.
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