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."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."
"Scalability-wise, I rate the solution a nine or ten 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."
"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."
"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."
"Its data preparation capabilities are highly valuable."
"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."
"It can scale to a great extent. It can handle the load that we are putting on it, which is about 5TBs."
"Matillion ETL is one hundred percent stable."
"We allow non-technical people to use Matillion to load data into our data warehouse for reporting. Thus, it is easy enough to use that we don't always have to get a technical person involved in setting up a data movement (ETL)."
"It's highly scalable. It takes upon itself the Redshift scalability, so it's very good."
"Matillion ETL helps manage data movement, ingestion, and transformation through pipelines."
"The technical support treats us well. They already have a support portal, and they are responsive, which helps."
"The simplicity of this tool is nice. It has a good graphical user interface. You can also do a lot of generic stuff in the tool. If there is good connectivity to a cloud database, such as Snowflake, and you can have a lot of Snowflake functionality in the tool."
"It is an incredibly user-friendly and intuitive tool, making the learning curve quite smooth"
"The solution could have more connectors."
"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."
"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 technical support could be a little better."
"The product is trying to be more maturity in terms of connectors. That, I believe, is an area where Cloud Pak can improve."
"Cloud Pak would be improved with integration with cloud service providers like Cloudera."
"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."
"Performance can be improved for efficiency, and it can be made faster."
"The cost of the solution is high and could be reduced."
"Unlike Snowflake which automatically takes care of upgrading to the latest version and includes additional features, with Matillion ETL we need to do this ourselves."
"The product must enhance its near-real-time data capture feature."
"It needs integration with more data sources."
"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."
"Going forward, I would like them to add custom jobs, since we still have to run these outside of Matillion."
"I am looking forward to seeing the expansion of the source range for their data loader product."
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, SSIS and Informatica PowerCenter. See our IBM Cloud Pak for Data vs. Matillion ETL report.
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