We performed a comparison between Denodo and IBM Cloud Pak for Data 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."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."
"It allows a lot of traceability and you can decide what data you want to collect"
"Denodo's best features are its performance, easy data transformation, and the job scheduler."
"The most valuable features are query optimization and the single language independence from the sources we're using to catch data."
"While we may not be using all the features of Denodo at this time, we have found the data virtualization features to be very useful in helping us connect our data sources together, bringing all our data into one platform."
"The data abstraction is the most valuable feature."
"Denodo makes it easy to export data as a service or data link to other services."
"Denodo is very stable."
"The most valuable features of IBM Cloud Pak for Data are the Watson Studio, where we can initiate more groups and write code. Additionally, Watson Machine Learning is available with many other services, such as APIs which you can plug the machine learning models."
"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."
"Its data preparation capabilities are highly valuable."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"What I found most helpful in IBM Cloud Pak for Data is containerization, which means it's easy to shift and leave in terms of moving to other clouds. That's an advantage of IBM Cloud Pak for Data."
"DataStage allows me to connect to different data sources."
"The most valuable feature of IBM Cloud Pak for Data is the Modeler flows. The ability to develop models using a graphical approach and the capability to connect to various sources, as well as the data virtualization capabilities, allow me to easily access and utilize data that is dispersed across different sources."
"It is a scalable solution, and we have had no issues with its scalability in our company. I rate the solution's scalability a nine out of ten."
"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."
"Denodo has some difficulty supporting large numbers of records."
"Documentation needs to be improved"
"We occasionally have some integration issues that we need to work through."
"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."
"Monitoring event logs can be improved. In the older version, there was a monitoring schedule to get event reports and properly audit the reports. In the newer version, it is not there, and we have to manually configure data and audit events."
"We can't scale it to meet digital requirements."
"We would like this solution to be more universally user-friendly. At present it is really only aimed at IT specialists."
"Cloud Pak would be improved with integration with cloud service providers like Cloudera."
"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."
"The product must improve its performance."
"There is a solution that is part of IBM Cloud Pak for Data called Watson OpenScale. It is used to monitor the deployed models for the quality and fairness of the results. This is one area that needs a lot of improvement."
"One challenge I'm facing with IBM Cloud Pak for Data is native features have been decommissioned, such as XML input and output. Too many changes have been made, and my company has around one hundred thousand mappings, so my team has been putting more effort into alternative ways to do things. Another area for improvement in IBM Cloud Pak for Data is that it's more complicated to shift from on-premise to the cloud. Other vendors provide secure agents that easily connect with your existing setup. Still, with IBM Cloud Pak for Data, you have to perform connection migration steps, upgrade to the latest version, etc., which makes it more complicated, especially as my company has XML-based mappings. Still, the XML input and output capabilities of IBM Cloud Pak for Data have been discontinued, so I'd like IBM to bring that back."
"One thing that bugs me is how much infrastructure Cloud Pak requires for the initial deployment. It doesn't allow you to start small. The smallest permitted deployment is too big. It's a huge problem that prevents us from implementing the solution in many scenarios."
"The solution's user experience is an area that has room for improvement."
"The technical support could be a little better."
Denodo is ranked 12th in Data Integration with 29 reviews while IBM Cloud Pak for Data is ranked 17th in Data Integration with 11 reviews. Denodo is rated 7.8, while IBM Cloud Pak for Data is rated 8.0. The top reviewer of Denodo writes "Saves our underwriters' time with data virtualization, but could provide more learning resources". On the other hand, the top reviewer of IBM Cloud Pak for Data writes "A scalable data analytics and digital transformation tool that provides useful features and integrations". Denodo is most compared with Azure Data Factory, AWS Glue, Delphix, Mule Anypoint Platform and TIBCO Data Virtualization, whereas IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Azure Data Factory, Informatica Cloud Data Integration, Palantir Foundry and IBM InfoSphere Information Server. See our Denodo vs. IBM Cloud Pak for Data report.
See our list of best Data Integration vendors and best Data Virtualization 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.