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."The most valuable feature of AWS Glue is that it provides a GUI format with a drag-and-drop feature."
"AWS Glue is quite better than other tools, but you have to learn it properly before you start using it."
"The product has a valuable feature for data catalog."
"The most valuable feature for me is the visual interface of AWS Glue."
"The solution is stable and reliable."
"It is AWS-integrated. There is end-to-end integration with the other AWS services. It is also user-friendly."
"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."
"It is a scalable solution."
"As long as you pick the solution that best fits with your requirements, you won't find that performance is a problem. It's good."
"It can scale."
"I have found it to be a very good, stable, and strong product."
"It's a stable solution."
"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."
"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."
"The technical support is very good. I rate the technical support a ten out of ten."
"The mapping area and the use of the data catalog from Glue could be better."
"AWS Glue would be improved by making it easier to switch from single to multi-cloud."
"One area that could be improved is the ETL view. The drag-and-drop interface is not as user-friendly as some other ETL tools."
"The solution should offer features for streaming data in addition to batching data."
"It fails to handle massive databases acquired from various sources."
"The start-up time is really high right now. For instance, when you start up a new job, you have to wait for five or eight minutes before it starts. If the start-up time is reduced to one or two minutes, it will be great. It will be better to have a direct linkage to Redshift in AWS. If we can use data catalogs from Redshift, it will be so easy to create some data catalogs. Currently, we can only use data catalogs from S3."
"Overall, I consider the technical support to be fine, although the response time could be faster in certain cases."
"The product has only a few built-in transformations."
"There could be more customization options."
"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."
"When processing data from certain tables with a large volume of data, we encounter significant delays. For instance, when dealing with around one million records, it typically takes three to four hours. To address this, I aim to implement performance improvements across all tables, ensuring swift processing similar to those that are currently complete within seconds. The performance issue primarily arises when we analyze the inserts and updates from the source, subsequently dropping the table. While new insertions are handled promptly, updates are processed slowly, leading to performance issues. Despite consulting our Qlik vendors, they were unable to pinpoint the exact cause of this occurrence. Consequently, I am seeking ways to optimize performance within Qlik Compose, specifically concerning updates."
"I'd like to have access to more developer training materials."
"There should be proper documentation available for the implementation process."
"It would be better if the first level of technical support were a bit more technically knowledgeable to solve the problem. I think they could also improve the injection of custom scripts. It is pretty difficult to add additional scripts. If the modeling doesn't give you what you want, and you want to change the script generated by the modeling, it is a bit more challenging than in most other products. It is very good with standard form type systems, but if you get a more complicated data paradigm, it tends to struggle with transforming that into a model."
"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."
"I believe that visual data flow management and the transformation function should be improved."
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Qlik Compose is ranked 20th 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, SSIS, Informatica Cloud Data Integration and Talend Open Studio, whereas Qlik Compose is most compared with Qlik Replicate, Talend Open Studio, Azure Data Factory, SSIS and WhereScape RED. See our AWS Glue vs. Qlik Compose report.
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