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."From what we have seen so far, the solution seems very stable."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"It is a complete ETL Solution."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
"It is a go-to tool for data virtualization. The virtualization and data catalog are the features of why we chose Denodo."
"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."
"Denodo is lightweight in terms of how it leads you to combine your discrete data systems at one spot."
"Denodo's best features are its performance, easy data transformation, and the job scheduler."
"The performance and the speed to market are the most valuable features of this solution."
"The most valuable features are data lineage and the concept of a semantic layer."
"Access to numerous forums and internet information."
"The data abstraction is the most valuable feature."
"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."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"We require Azure Data Factory to be able to connect to Google Analytics."
"There aren't many third-party extensions or plugins available in the solution."
"Real-time replication is required, and this is not a simple task."
"The support is not the best and should be improved."
"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 integration could use improvement, it's a lot of non-speed line processes that we have discovered, in the country. The configurations could use a lot more improvement."
"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"
"It would be good if the solution provided a much-needed cellular platform."
"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."
"Lacks integrations with AWS, GCP and the like."
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.
See our list of best Data Integration vendors.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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.