We performed a comparison between Azure Data Factory 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."The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"The most valuable aspect is the copy capability."
"The scalability of the product is impressive."
"The solution has a good interface and the integration with GitHub is very useful."
"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 complete ETL Solution."
"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"It's extremely consistent."
"DataStage allows me to connect to different data 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."
"You can model the data there, connect the data models with the business processes and create data lineage processes."
"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."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"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."
"The most valuable features are data virtualization and reporting."
"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."
"The initial setup is not very straightforward."
"There aren't many third-party extensions or plugins available in the solution."
"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."
"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."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"I would like to be informed about the changes ahead of time, so we are aware of what's coming."
"The solution's user experience is an area that has room for improvement."
"The technical support could be a little better."
"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 interface could improve because sometimes it becomes slow. Sometimes there is a delay between clicks when using the software, which can make the development process slow. It can take a few seconds to complete one action, and then a few more seconds to do the next one."
"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."
"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."
"The solution could have more connectors."
"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."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while IBM Cloud Pak for Data is ranked 17th in Data Integration with 11 reviews. Azure Data Factory is rated 8.0, while IBM Cloud Pak for Data is rated 8.0. 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 IBM Cloud Pak for Data writes "A scalable data analytics and digital transformation tool that provides useful features and integrations". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Informatica Cloud Data Integration, Palantir Foundry, Denodo and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM Cloud Pak for Data report.
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