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 trigger scheduling options are decently robust."
"The function of the solution is great."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The most valuable aspect is the copy capability."
"The security of the agent that is installed on-premises is very good."
"The solution has a good interface and the integration with GitHub is very useful."
"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"Powerful but easy-to-use and intuitive."
"In general, it's good for us to make tests so we can scout the data."
"This solution provides us with the ability to sync data, and make it available for anyone to use across the business."
"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."
"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 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."
"Data mining is one of the valuable features. We're able to connect all of the data sources with the installed driver, so that is a good advantage in Denodo. Being able to join the tables and view them is also valuable."
"The data abstraction is the most valuable feature."
"The most valuable features are query optimization and the single language independence from the sources we're using to catch data."
"Data Factory's monitorability could be better."
"The product could provide more ways to import and export data."
"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"The deployment should be easier."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"Denodo has some difficulty supporting large numbers of records."
"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."
"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."
"We would like this solution to be more universally user-friendly. At present it is really only aimed at IT specialists."
"I would like to see a proper way to avoid killing the sourcing systems."
"I would like to see a connectivity option with third-party apps, for example, JDBC, and ODBC drivers. Currently, we need to install it separately from the Denodo side and then connect it."
"The solution is slow when there are many virtualization layers."
"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, 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.