We performed a comparison between CloverETL and Pentaho Data Integration and Analytics 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."Connectivity to various data sources: The ability to extract data from different data sources gives greater flexibility."
"Key features include wealth of pre-defined components; all components are customizable; descriptive logging, especially for error messages."
"Server features for scheduler: It is very easy to schedule jobs and monitor them. The interface is easy to use."
"No dependence on native language and ease of use."
"Data transformation within Pentaho is a nice feature that they have and that I value."
"The product is user-friendly and intuitive"
"One of the valuable features is the ability to use PL/SQL statements inside the data transformations and jobs."
"We use Lumada’s ability to develop and deploy data pipeline templates once and reuse them. This is very important. When the entire pipeline is automated, we do not have any issues in respect to deployment of code or with code working in one environment but not working in another environment. We have saved a lot of time and effort from that perspective because it is easy to build ETL pipelines."
"The way it has improved our product is by giving our users the ability to do ad hoc reports, which is very important to our users. We can do predictive analysis on trends coming in for contracts, which is what our product does. The product helps users decide which way to go based on the predictive analysis done by Pentaho. Pentaho is not doing predictions, but reporting on the predictions that our product is doing. This is a big part of our product."
"We're using the PDI and the repository function, and they give us the ability to easily generate reporting and output, and to access data. We also like the ability to schedule."
"We also haven't had to create any custom Java code. Almost everywhere it's SQL, so it's done in the pipeline and the configuration. That means you can offload the work to people who, while they are not less experienced, are less technical when it comes to logic."
"The abstraction is quite good."
"Needs: easier automated failure recovery; more, and more intuitive auto-generated/filled-in code for components; easier/more automated sync between CloverETL Designer and CloverETL Server."
"Resource management: We typically run out of heap space, and even the allocation of high heap space does not seem to be enough."
"Its documentation could be improved."
"Its basic functionality doesn't need a whole lot of change. There could be some improvement in the consistency of the behavior of different transformation steps. The software did start as open-source and a lot of the fundamental, everyday transformation steps that you use when building ETL jobs were developed by different people. It is not a seamless paradigm. A table input step has a different way of thinking than a data merge step."
"I would like to see support for some additional cloud sources. It doesn't support Azure, for example. I was trying to do a PoC with Azure the other day but it seems they don't support it."
"It could be better integrated with programming languages, like Python and R. Right now, if I want to run a Python code on one of my ETLs, it is a bit difficult to do. It would be great if we have some modules where we could code directly in a Python language. We don't really have a way to run Python code natively."
"Since Hitachi took over, I don't feel that the documentation is as good within the solution. It used to have very good help built right in."
"Should provide additional control for the data warehouse"
"I would like to see improvements made for real-time data processing."
"Lumada could have more native connectors with other vendors, such as Google BigQuery, Microsoft OneDrive, Jira systems, and Facebook or Instagram. We would like to gather data from modern platforms using Lumada, which is a better approach. As a comparison, if you open Power BI to retrieve data, then you can get data from many vendors with cloud-native connectors, such as Azure, AWS, Google BigQuery, and Athena Redshift. Lumada should have more native connectors to help us and facilitate our job in gathering information from these new modern infrastructures and tools."
"Although it is a low-code solution with a graphical interface, often the error messages that you get are of the type that a developer would be happy with. You get a big stack of red text and Java errors displayed on the screen, and less technical people can get intimidated by that. It can be a bit intimidating to get a wall of red error messages displayed. Other graphical tools that are focused at the power user level provide a much more user-friendly experience in dealing with your exceptions and guiding the user into where they've made the mistake."
More Pentaho Data Integration and Analytics Pricing and Cost Advice →
Earn 20 points
CloverETL is ranked 61st in Data Integration while Pentaho Data Integration and Analytics is ranked 15th in Data Integration with 49 reviews. CloverETL is rated 7.0, while Pentaho Data Integration and Analytics is rated 8.0. The top reviewer of CloverETL writes "Provides wealth of pre-defined, customizable components, and descriptive logging for errors". On the other hand, the top reviewer of Pentaho Data Integration and Analytics writes "It's flexible and can do almost anything I want it to do". CloverETL is most compared with SSIS, iWay Universal Adapter Framework and Talend Open Studio, whereas Pentaho Data Integration and Analytics is most compared with SSIS, Azure Data Factory, Oracle Data Integrator (ODI), Talend Open Studio and AWS Glue. See our CloverETL vs. Pentaho Data Integration and Analytics 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.
