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 tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"Data Factory's best features are simplicity and flexibility."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"The trigger scheduling options are decently robust."
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"The user interface is very good. It makes me feel very comfortable when I am using the tool."
"Denodo is very stable."
"It allows a lot of traceability and you can decide what data you want to collect"
"In PL/SQL, first you need to gather all the data and then start writing the file, but in Denodo you fetch the data and write the data simultaneously. So, for example, if you have 1 million or 2 million records, you don't have to wait to fetch all of the 2 million; you can keep on fetching and writing in the file simultaneously."
"The data abstraction is the most valuable feature."
"The best thing about Denodo is that creating and deploying a web service can be done in about 10 minutes, compared to a whole day when it comes to other solutions (such as when deploying with Java and AWS)."
"Denodo is lightweight in terms of how it leads you to combine your discrete data systems at one spot."
"It is easy to virtualize data using the solution."
"The most valuable feature is Data Catalogs."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"In the next release, it's important that some sort of scheduler for running tasks is added."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"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."
"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."
"We occasionally have some integration issues that we need to work through."
"The dropdown menus feel antiquated to me, and the administrative portals need improvement."
"The git configuration really should be improved."
"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"
"Sometimes, Windows-related functions do not work properly in Denodo. The analytic functions in SQL do not work properly."
"The feature that you have to connect on LDAP needs improvement."
"There have been some issues when you are at a table. Currently, Denodo exports data sets for a tabular model. When you are finished modeling your database or data warehouse they export a link to be used in Tableau. They should support other tools like Power BI."
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.