We performed a comparison between AWS Data Pipeline [EOL] 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 the solution is that orchestration and development capabilities are easier with the tool."
"It is a stable solution...It is a scalable solution."
"The technical support is very good. I rate the technical support a ten out of ten."
"One of the most valuable features was the ability to integrate multiple source systems that mainly used structured IDBMS versions."
"Qlik Compose is good enough. It is user-friendly and intuitive."
"It can scale."
"I have found it to be a very good, stable, and strong product."
"It's a stable 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 is a scalable solution."
"The user-defined functions have shortcomings in AWS Data Pipeline."
"It's almost semi-automatic because you must review and approve code push, which works well. Still, we had many problems getting there during the deployment process, but we got there."
"I'd like to have access to more developer training materials."
"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."
"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."
"There could be more customization options."
"Qlik's ETL and data transformation could be better."
"My issues with the solution's stability are owing to the fact that it has certain bugs causing issues in some functionalities that should be working."
"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 believe that visual data flow management and the transformation function should be improved."
AWS Data Pipeline [EOL] doesn't meet the minimum requirements to be ranked in Cloud Data Integration with 2 reviews while Qlik Compose is ranked 20th in Data Integration with 12 reviews. AWS Data Pipeline [EOL] is rated 8.0, while Qlik Compose is rated 7.6. The top reviewer of AWS Data Pipeline [EOL] writes "A tool with great orchestration and development capabilities but needs to improve its user-defined functions". On the other hand, the top reviewer of Qlik Compose writes "Easy matching and reconciliation of data". AWS Data Pipeline [EOL] is most compared with AWS Database Migration Service, AWS Glue, Oracle Data Integrator (ODI), FME and Perspectium DataSync, whereas Qlik Compose is most compared with Qlik Replicate, Talend Open Studio, Azure Data Factory, SSIS and Palantir Foundry. See our AWS Data Pipeline [EOL] vs. Qlik Compose report.
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