We performed a comparison between Azure Data Factory and erwin Data Catalog by Quest based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."From what we have seen so far, the solution seems very stable."
"I like the basic features like the data-based pipelines."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"The most important feature is that it can help you do the multi-threading concepts."
"In terms of my personal experience, it works fine."
"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."
"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."
"The data catalog feature is pretty good."
"When you combine it with data lineage, every time you need to make a change, it allows you to do impact analysis on any changes and then connect to the end-users or data stewards so that they can be aware that a change is coming. That's one of the main benefits we use it for."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"There aren't many third-party extensions or plugins available in the solution."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"There is no built-in pipeline exit activity when encountering an error."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"Lacks in-built streaming data processing."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"There is room for improvement with respect to the connector and how to connect to the structured and unstructured database."
"There are always ways to improve things. For example, we can use AI to be able to find out something. When we are typing something, if we don't know the exact term, Artificial Intelligence would be useful to find terms that are phonetically or syntactically similar. Instead of having to type in the exact name, they can provide those in the list. So, they can provide AI support for the search because when you have thousands and thousands of terms, it is hard to remember all the names."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while erwin Data Catalog by Quest is ranked 12th in Metadata Management with 2 reviews. Azure Data Factory is rated 8.0, while erwin Data Catalog by Quest is rated 7.6. 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 erwin Data Catalog by Quest writes "Helps with metadata management, saves time, and allows us to do impact analysis on any changes". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas erwin Data Catalog by Quest is most compared with Informatica Enterprise Data Catalog, Talend Open Studio, Alation Data Catalog and Oracle Data Integrator (ODI).
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
