We performed a comparison between IBM Cloud Pak for Data and Oracle GoldenGate 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."Its data preparation capabilities are highly valuable."
"Scalability-wise, I rate the solution a nine or ten out of ten."
"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."
"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."
"DataStage allows me to connect to different data sources."
"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."
"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."
"They've recently improved the ease of implementation."
"GoldenGate can connect and collect data from multiple sources, such as SQL Server."
"Data migration is the most valuable feature of Oracle GoldenGate."
"It's very simple to configure, it's very simple to implement. In addition, the ability it has to capture data and transmit it with incredible speed is better than any of the product out there. It's extremely powerful."
"The replication of the data table is one of the best features."
"It works best with Oracle."
"Ease of installation, maintenance and powerful outputs and supporting Big Data and Cloud environment as well as OGGCS (Oracle Golden Gate Cloud Service)."
"The product is reliable for data integrity."
"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."
"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."
"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 technical support could be a little better."
"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 tool depends on the control plane, an OpenShift container platform utilized as an orchestration layer...So, we have communicated this issue to IBM and asked if it is feasible to adapt the solution to work on a Kubernetes platform that we support."
"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."
"The solution's licensing price is very expensive and could be made more competitive."
"It should be easier to integrate this solution with non-Oracle databases, including cloud-based solutions hosted on Azure."
"Technical support for Oracle products needs to be more efficient (at least locally in Egypt)."
"The solution needs to improve its latency, monitoring and support."
"The front-end management isn't very good."
"We struggle with memory. It's limited. However, it may be because of our unique business case and how we use it that it's limiting for us."
"The solution costs a lot."
"The problem with GoldenGate is it is very complex to use. You need heavyweight skills to use it."
IBM Cloud Pak for Data is ranked 17th in Data Integration with 11 reviews while Oracle GoldenGate is ranked 6th in Data Integration with 48 reviews. IBM Cloud Pak for Data is rated 8.0, while Oracle GoldenGate is rated 8.2. 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 Oracle GoldenGate writes "Performs real-time replication without data loss, but we cannot do much automation". IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Azure Data Factory, Informatica Cloud Data Integration, Palantir Foundry and Denodo, whereas Oracle GoldenGate is most compared with AWS Database Migration Service, Qlik Replicate, Quest SharePlex, Azure Data Factory and Oracle Data Integrator (ODI). See our IBM Cloud Pak for Data vs. Oracle GoldenGate 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.
