We performed a comparison between Azure Data Factory and Toad Data Point 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."The most valuable feature is the copy activity."
"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 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."
"I can do everything I want with SSIS and Azure Data Factory."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"It is a complete ETL Solution."
"From what we have seen so far, the solution seems very stable."
"Allows more data between on-premises and cloud solutions"
"The Connectivity and Connection Manager supports a broad number of connection types, and it is trivial for end-users to set up their own connections to sources."
"The most valuable features of Toad Data are you could write a parameterized query and it wouldn't error out, it would give you the parameters that you could input. The auto-formatting feature is useful because it was great for keeping your queries neat and understandable. The auto comment, and uncomment toggles that you could do were convenient."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"The one element of the solution that we have used and could be improved is the user interface."
"Data Factory could be improved in terms of data transformations by adding more metadata extractions."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"The product could provide more ways to import and export data."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"Toad Data could improve by having additional features, such as query prediction. This could help someone who's not the strongest programmer. If the software could help them write queries correctly it would be very helpful, especially for small development teams or teams that lack the input skills necessary to write and program efficiently."
"On the scheduling server, some scheduled reports just sit there and never execute for the first time. After manually executing the first time, they run with no issues."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Toad Data Point is ranked 39th in Data Integration with 2 reviews. Azure Data Factory is rated 8.0, while Toad Data Point is rated 9.0. 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 Toad Data Point writes "Easy to learn, good connectivity to multiple data sources, and helpful supprt". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Toad Data Point is most compared with SSIS, SAS Enterprise Guide, Alteryx, Oracle Data Integrator (ODI) and Denodo. See our Azure Data Factory vs. Toad Data Point report.
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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.
