We performed a comparison between Azure Data Factory and Qlik Replicate based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It is beneficial that the solution is written with Spark as the back end."
"The flexibility that Azure Data Factory offers is great."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"The CDC and the flexibility to use QVD as a source are the most valuable features of Qlik Replicate."
"A pretty good series of connectors is one of the best features of Qlik Replicate."
"Qlik Replicate stands out with its cutting-edge technology and its ability to handle diverse data management tasks. This powerful tool allows us to efficiently and swiftly load data into various data stores or destinations, while also enabling easy distribution across different endpoints. A notable feature is its capability to reload data from multiple sources by creating multiple tasks within a brief timeframe of fifteen to twenty minutes. This eliminates the need for manual intervention and ensures quick data loading from different tables."
"It's very user-friendly when it comes to settings and configuration. It also offers very detailed logging about warnings and errors."
"We use Qlik Replicate to change data capture of databases in production environments."
"It enables us to transform data at the latest stage rather than in ETL loads, so it's more ELT which is one of the advantages. It is also in near real-time, which brings significant advantage for our embedded analytics approach."
"The most useful functions of Qlik Replicate are the data manipulation to transformations."
"From a technical perspective, this is an excellent product."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"The pricing scheme is very complex and difficult to understand."
"I have not found any real shortcomings within the product."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"Some known bugs and issues with Azure Data Factory could be rectified."
"In the next release, I would like to see closer integration with data catalyst."
"In various scenarios, an important consideration is when we encounter issues and Qlik Replicate suggests reloading a specific table. If we face any problems or encounter errors with that table, it becomes necessary to make a change in Qlik Replicate. Performing a full reload every time is not feasible or practical. Instead, we should identify the specific issues and address them without repeating the entire reloading process. Based on this approach, we can investigate and resolve the problem by performing targeted loads from the source itself. This change aligns with my perspective and is something I would like to implement."
"We'd like better connectivity."
"Support-wise, this solution is in need of improvement."
"Support for this product is not great. It needs to be improved."
"The solution's flexibility to work with APIs should also be improved since it is very weak in working with APIs."
"It's not possible to replicate the QVC files in data analytics."
"The UI and data version control can be improved."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Qlik Replicate is ranked 17th in Data Integration with 12 reviews. Azure Data Factory is rated 8.0, while Qlik Replicate is rated 8.2. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Qlik Replicate writes "A highly stable solution that can be used to change data capture in legacy systems". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas Qlik Replicate is most compared with Oracle GoldenGate, AWS Database Migration Service, Qlik Compose, Fivetran and SSIS. See our Azure Data Factory vs. Qlik Replicate report.
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