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."Its integrability with the rest of the activities on Azure is most valuable."
"The initial setup is very quick and easy."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"Data Factory's best features are simplicity and flexibility."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The most valuable features are data transformations."
"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."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"Overall, the product works quite well and has a good set of features."
"It allows a lot of traceability and you can decide what data you want to collect"
"Denodo is lightweight in terms of how it leads you to combine your discrete data systems at one spot."
"The most valuable features are data lineage and the concept of a semantic layer."
"The ability to connect to a lot of different sources."
"The most valuable features are query optimization and the single language independence from the sources we're using to catch data."
"It is a very stable solution."
"This solution provides us with the ability to sync data, and make it available for anyone to use across the business."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Data Factory could be improved in terms of data transformations by adding more metadata extractions."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"I would like to see a proper way to avoid killing the sourcing systems."
"The dropdown menus feel antiquated to me, and the administrative portals need improvement."
"It would be beneficial to make sure that the team that will be using Denodo has some kind of training on how to use the product at least a month beforehand, and there could even be some kind of feedback or Q&A sessions to go along with the training. If Denodo were able to provide this kind of training, it would be very helpful to users in insurance and banking companies because the staff are typically older and not always technically-minded."
"The solution should have its own acceleration technology."
"The feature that you have to connect on LDAP needs improvement."
"It would be good if the solution provided a much-needed cellular platform."
"The data catalog certainly has room for improvement. It is functional but we look forward to development. We are in constant contact with Denodo and they are fully aware of our needs."
"Documentation needs to be improved"
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, Mule Anypoint Platform, Delphix, 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.