We performed a comparison between IBM Cloud Pak for Data and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."You can model the data there, connect the data models with the business processes and create data lineage processes."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"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."
"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."
"Its data preparation capabilities are highly valuable."
"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."
"Cloud Pak's most valuable features are IBM MQ, IBM App Connect, IBM API Connect, and ISPF."
"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
"The most valuable feature is real-time streaming."
"The product is very user-friendly."
"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."
"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."
"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."
"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 product must improve its performance."
"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's user experience is an area that has room for improvement."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
IBM Cloud Pak for Data is ranked 17th in Data Integration with 11 reviews while Spring Cloud Data Flow is ranked 28th in Data Integration with 5 reviews. IBM Cloud Pak for Data is rated 8.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of IBM Cloud Pak for Data writes "A scalable data analytics and digital transformation tool that provides useful features and integrations". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Azure Data Factory, Informatica Cloud Data Integration, Palantir Foundry and Denodo, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and TIBCO BusinessWorks.
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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.
