We performed a comparison between AWS Glue and Qlik Compose 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."I appreciate AWS Glue for its cost-effectiveness."
"The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team."
"The most valuable feature for me is the visual interface of AWS Glue."
"The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs."
"We no longer had to worry much about infrastructure management because AWS Glue is serverless, and Amazon takes care of the underlying infrastructure."
"I like its integration and ability to handle all data-related tasks."
"AWS Glue's best features are scalability and cloud-based features."
"The most valuable features currently are glue studio, jobs, and triggers."
"There were many valuable features, such as extracting any data to put in the cloud. For example, Qlik was able to gather data from SAP and extract SAP data from the platforms."
"Qlik Compose is good enough. It is user-friendly and intuitive."
"I like modeling and code generation. It has become a pretty handy tool because of its short ideation to delivery time. From the time you decide you are modeling a data warehouse, and once you finish the modeling, it generates all the code, generates all the tables. All you have to do is tick a few things, and you can produce a fully functional warehouse. I also like that they have added all the features I have asked for over four years."
"The most valuable is its excellence as a graphical data representation tool and the versatility it offers, especially with drill-down capabilities."
"It is a scalable solution."
"It's a stable solution."
"The technical support is very good. I rate the technical support a ten out of ten."
"One of the most valuable features of this tool is its automation capabilities, allowing us to design the warehouse in an automated manner. Additionally, we can generate Data Lifecycle Policies (DLP) reports and efficiently implement updates and best practices based on proven design patterns."
"Glue could perform better. It sometimes takes too long to test a Glue job. Google Cloud Platform offers more Python scripts than AWS."
"The solution could be cheaper. The price of the solution is an area that needs improvement."
"I would like to see stable libraries at the moment they are not there."
"In terms of performance, if they can further optimize the execution time for serverless jobs, it would be a welcome improvement."
"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 have encountered challenges with multi-region support."
"AWS Glue is more costly compared to other tools like Airflow."
"I haven't looked into Glue in terms of seeking out flaws. I've not come across missing features."
"For more complex work, we are not using Qlik Compose because it cannot handle very high volumes at the moment. It needs the same batching capabilities that other ETL tools have. We can't batch the data into small chunks when transforming large amounts of data. It tries to do everything in one shot and that's where it fails."
"There could be more customization options."
"There is some scope for improvement around the documentation, and a better UI would definitely help."
"There should be proper documentation available for the implementation process."
"It could enhance its capabilities in the realm of self-service options as currently, it is more suited for individuals with technical proficiency who can create pages using it."
"I'd like to have access to more developer training materials."
"Qlik's ETL and data transformation could be better."
"The solution has room for improvement in the ETL. They have an ETL, but when it comes to the monitoring portion, Qlik Compose doesn't provide a feature for monitoring."
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Qlik Compose is ranked 18th in Data Integration with 12 reviews. AWS Glue is rated 7.8, while Qlik Compose 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 Qlik Compose writes "Easy matching and reconciliation of data". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, Informatica Cloud Data Integration, SSIS and Talend Open Studio, whereas Qlik Compose is most compared with Qlik Replicate, Talend Open Studio, Oracle Data Integrator (ODI), Azure Data Factory and Informatica Enterprise Data Catalog. See our AWS Glue vs. Qlik Compose report.
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