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 most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"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 is the ease in which you can create an ETL pipeline."
"It is beneficial that the solution is written with Spark as the back end."
"I am one hundred percent happy with the stability."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"Access to numerous forums and internet information."
"Denodo is very stable."
"Denodo's best features are its performance, easy data transformation, and the job scheduler."
"The most valuable feature is Data Catalogs."
"The ability to transfer data is very valuable."
"The most valuable features are data lineage and the concept of a semantic layer."
"The logical data warehouse functionality is fantastic. It truly stands out. The ClearOptimizer and Virtual Cache are great features. They work together seamlessly to optimize performance."
"It can support a number of data sources, and it can pull flat files, from cloud-based databases or from those on-premises. Denodo can pull from any data source and interface with the view. Then, we can publish the view."
"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 deployment should be easier."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Data Factory's performance during heavy data processing isn't great."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"There's space for improvement in the development process of the data pipelines."
"There are limitations when processing more than one GD file."
"Tasks such as conversion of a date format or conversion of a number format that can be done in a very easy way in different languages, like SQL or Oracle, are not so easy to do in Denodo. For example, if you want to convert a date from one format to another, in Oracle it's pretty easy; in Denodo, however, it requires so many lines of code. Simple things that can be done very quickly in other database languages require more lines of code in Denodo."
"The dropdown menus feel antiquated to me, and the administrative portals need improvement."
"The git configuration really should be improved."
"Lacks integrations with AWS, GCP and the like."
"The feature that you have to connect on LDAP needs improvement."
"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."
"Documentation needs to be improved"
"User-specific security at the column and row levels needs to be improved. Instead of applying security at every individual level, it would be better if it were at the group or tier level. It will save a lot of time."
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.