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 overall performance is quite good."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"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."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The best part of this product is the extraction, transformation, and load."
"Data Factory's best features are simplicity and flexibility."
"The solution is okay."
"It is a go-to tool for data virtualization. The virtualization and data catalog are the features of why we chose Denodo."
"One thing that we have noticed is that when you have a BI tool, you end up building a lot of the logic in the BI tool, but as a company, every company wants to be tool agnostic because today, you could be in the Qlik Sense, and tomorrow, you may decide to go with Tableau or something else that is there. If you have put a lot of logic within the tool, transitioning or moving away from one BI tool to another tool becomes a very intensive process. By keeping the logic in Denodo, you can move to any tool."
"The most valuable feature is the performance. Denodo is very useful, especially in this huge pharma environment. I've found that older SAP solutions were very tightly coupled to each other, which resulted in data restrictions. Getting data from different sources was tough and tedious. Compared to these old solutions, Denodo is very easy to work with for the analytical team. Now that we've implemented this virtualization layer, we are capable of getting the data very smoothly. We implemented a very small unit, but the performance and integration have been very good."
"The ability to connect to a lot of different sources."
"The data abstraction is the most valuable feature."
"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."
"The ability to transfer data is very valuable."
"Denodo's best features are its performance, easy data transformation, and the job scheduler."
"The initial setup is not very straightforward."
"It would be better if it had machine learning capabilities."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"I have not found any real shortcomings within the product."
"Some known bugs and issues with Azure Data Factory could be rectified."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"Performance management could be improved."
"We occasionally have some integration issues that we need to work through."
"The solution is slow when there are many virtualization layers."
"Documentation needs to be improved"
"I would like to see a proper way to avoid killing the sourcing systems."
"Denodo has some difficulty supporting large numbers of records."
"The git configuration really should be improved."
"The dropdown menus feel antiquated to me, and the administrative portals need 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.