We performed a comparison between IBM Cloud Pak for Data and Matillion ETL based on real PeerSpot user reviews.
Find out in this report how the two Data Virtualization solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."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."
"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."
"DataStage allows me to connect to different data sources."
"Its data preparation capabilities are highly valuable."
"Scalability-wise, I rate the solution a nine or ten out of ten."
"One of Cloud Pak's best features is the Watson Knowledge Catalog, which helps you implement data governance."
"You can model the data there, connect the data models with the business processes and create data lineage processes."
"Matillion ETL has great Git integration that is perfect and convenient to use."
"The most valuable feature of Matillion ETL is the ETL. The solution is open-source which provides advantages, such as good performance and high efficiency. Additionally, it supports three data types which eliminates predefining the data, and we can write script models in Python."
"The technical support treats us well. They already have a support portal, and they are responsive, which helps."
"The most valuable feature of Matillion ETL is the UI experience in which you can drag and drop most of the transformation."
"The loading of data is the most valuable feature of Matillion ETL."
"Matillion ETL helps manage data movement, ingestion, and transformation through pipelines."
"It has helped us to get onto the cloud quickly."
"It's highly scalable. It takes upon itself the Redshift scalability, so it's very good."
"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."
"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 solution could have more connectors."
"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."
"Cloud Pak would be improved with integration with cloud service providers like Cloudera."
"The solution's user experience is an area that has room for improvement."
"The cost of the solution is high and could be reduced."
"Sometimes, we have issues with the solution's stability and need to restart it for three weeks or more."
"Performance can be improved for efficiency, and it can be made faster."
"Matillion’s on-premises capabilities don’t allow you to build something customized."
"While the UI is good, it could be improved in its efficiency and made easier to use."
"To complete the pipeline, they might want to include some connectors which would put the data into different platforms. This would be helpful."
"The improvement area could be possible if the tool provides better integration capabilities with other ecosystems, including governance tools or data cataloging tools, as it is currently an area where the solution is lacking."
"Our main challenge currently is that Matillion runs on an EC2 instance, limiting us to running only two processes simultaneously at the entry level."
IBM Cloud Pak for Data is ranked 3rd in Data Virtualization with 11 reviews while Matillion ETL is ranked 4th in Cloud Data Integration with 24 reviews. IBM Cloud Pak for Data is rated 8.0, while Matillion ETL is rated 8.6. 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 Matillion ETL writes "Efficient data integration and transformation with seamless cloud-native integration". IBM Cloud Pak for Data is most compared with IBM InfoSphere DataStage, Azure Data Factory, Informatica Cloud Data Integration, Palantir Foundry and Denodo, whereas Matillion ETL is most compared with Snowflake, Azure Data Factory, AWS Glue, Informatica PowerCenter and SSIS. See our IBM Cloud Pak for Data vs. Matillion ETL report.
We monitor all Data Virtualization 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.
