We performed a comparison between Azure Data Factory and Denodo 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 data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"The trigger scheduling options are decently robust."
"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."
"Denodo is lightweight in terms of how it leads you to combine your discrete data systems at one spot."
"The ability to transfer data is very valuable."
"While we may not be using all the features of Denodo at this time, we have found the data virtualization features to be very useful in helping us connect our data sources together, bringing all our data into one platform."
"Denodo makes it easy to export data as a service or data link to other services."
"The performance and the speed to market are the most valuable features of this solution."
"The most valuable features of Denodo are the extraction option for adapters, and there are many things for the views, that are cached. Denodo is not storing the data, it looks first to tune the query, and these things are for the agents."
"Access to numerous forums and internet information."
"Overall, the product works quite well and has a good set of features."
"Some known bugs and issues with Azure Data Factory could be rectified."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"There is no built-in pipeline exit activity when encountering an error."
"The one element of the solution that we have used and could be improved is the user interface."
"The support and the documentation can be improved."
"There's space for improvement in the development process of the data pipelines."
"Data Factory's cost is too high."
"Sometimes, Windows-related functions do not work properly in Denodo. The analytic functions in SQL do not work properly."
"Performance management could be improved."
"The solution should have its own acceleration technology."
"We can't scale it to meet digital requirements."
"Denodo's training documentation could be improved by providing more material. From an administrative standpoint, I've found that only Denodo websites provide the usual tutorials. It may be because it's a bit of a restricted tool, but it results in trouble with learning. Normally, I can find help and solutions from other sources, but I haven't been able to find any for Denodo. Other that, it's fine and it performs well. I only have six months of experience, so I can't accurately suggest improvements."
"We would like this solution to be more universally user-friendly. At present it is really only aimed at IT specialists."
"Denodo can improve usage management-related aspects. If you deal with the mini views, it gets stuck. The performance is very slow when we go with a large number of views and high volume."
"Denodo currently integrates with ChatGPT, but the ability to manage and utilize them directly within Denodo would be a significant improvement."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Denodo is ranked 12th in Data Integration with 29 reviews. Azure Data Factory is rated 8.0, while Denodo is rated 7.8. 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 Denodo writes "Saves our underwriters' time with data virtualization, but could provide more learning resources". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Oracle GoldenGate, whereas Denodo is most compared with AWS Glue, Delphix, Mule Anypoint Platform, Informatica PowerCenter and Palantir Foundry. See our Azure Data Factory vs. Denodo report.
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Greetings, Stefan.
Alteryx is basically an ETL tool that evolved to deliver some Data Viz and ML features too. This means that its main purpose is to extract data from different sources, combine and transform them and finally load them in a different database.
Denodo is a data virtualization tool, which means it does all the transformations without extracting from one place and loading to another one. It´s a cloud-based solution and it charges by the traffic. If your company has specific General Data Protection Regulation that prohibits for instance that you extract the data located in a data center in Europe and loading them in a cluster located in the USA, you will probably need a virtualization tool like Denodo instead of an ETL like Alteryx. Virtualization tools are usually more expensive in a long run
Azure Data Factory is a platform meant to leverage the use of Azure. Microsoft´s objective is to sell its cloud solution as a whole. It contains a Data Studio (to manage and control your data), SPARK (which is a Hadoop in memory) and a data lake storage.
As you see, those are 3 different products that do not make much sense to be used together.
I'd say that there is a misconception in some of the answers (but don't worry, it's a common one).
Alteryx is not an ETL tool, it's an analytics platform with very powerful ETL capabilities (accessing mostly all data sources available and processing them at high speeds among others).
But additionally, Alteryx gives you the ability to carry on with the complete analytics cycle, processing, cleaning, blending those diverse data sources, modeling descriptive, predictive, prescriptive analytics (plus some ML & AI), outputting to another humongous variety of data sources, reporting or visualization tools.
All of the previous can be achieved with no coding at all, but in case you want to code, Alteryx also offers Python, R & Scala native integration. In other words, it can solve business users' use cases and advanced/technical use cases at the same time.
Finally, it's a fixed license, with no additional costs per usage (at least so far, until they release the Cloud Version).
I hope I was able to clarify the role of Alteryx in the analytics landscape.