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."It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"I am one hundred percent happy with the stability."
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"The most valuable feature is the copy activity."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The most valuable feature of this solution would be ease of use."
"It is easy to deploy workflows and schedule jobs."
"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."
"You can model the data there, connect the data models with the business processes and create data lineage processes."
"DataStage allows me to connect to different data sources."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"Scalability-wise, I rate the solution a nine or ten out of ten."
"Its data preparation capabilities are highly valuable."
"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."
"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."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"Currently, our company requires a monitoring tool, and that isn't available in Azure Data Factory."
"Data Factory's monitorability could be better."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"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 must improve its performance."
"Cloud Pak would be improved with integration with cloud service providers like Cloudera."
"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."
"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 technical support could be a little better."
"The solution's user experience is an area that has room for improvement."
"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 16th 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 IBM InfoSphere DataStage, 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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