We performed a comparison between Syniti Data Quality and Talend Data Quality based on real PeerSpot user reviews.
Find out in this report how the two Data Quality solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."With Syniti Data Quality, you can integrate SAP and directly fix errors from Syniti Data Quality instead of logging into SAP and then fixing them."
"The customer service and support is good."
"Syniti has built-in 80% of the solution, and we only need to customize 20 to 25% of the features. It is easy to run and pre-load reports."
"The major benefits of Syniti Data Quality stem from the productivity and flexibility it offers to users."
"The solution enables robust data matching, merging, survivorship, and Data Stewardship that can be a part of data quality workflows or true master data management."
"The jobs are visual and this has improved collaboration between colleagues. It’s much easier to understand a visual job than a piece of Java code."
"It has definitely streamlined certain processes."
"It reduces the QA effort immensely by handling most of the test scenarios in a reusable way."
"I really like the fact that there are no out-of-the-box solutions regarding the development of jobs. Other vendors may have modules which cleanse your addresses. In Talend, you have the freedom to completely develop the process yourself. This can be tricky, but it also makes it fun."
"The features that I find to be the most valuable are the extensibility, the integration, and the ease of integration with multiple platforms."
"The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work."
"It is saving a lot of time. Today, we can mask around a hundred million records in 10 minutes. Masking is one of the key pieces that is used heavily by the business and IT folks. Normally in the software development life cycle, before you project anything into the production environment, you have to test it in the test environment to make sure that when the data goes into production, it works, but these are all production files. For example, we acquired a new company or a new state for which we're going to do the entire back office, which is related to claims processing, payments, and member enrollment every year. If you get the production data and process it again, it becomes a compliance issue. Therefore, for any migrations that are happening, we have developed a new capability called pattern masking. This feature looks at those files, masks that information, and processes it through the system. With this, there is no PHI and PII element, and there is data integrity across different systems. It has seamless integration with different databases. It has components using which you can easily integrate with different databases on the cloud or on-premise. It is a drag and drop kind of tool. Instead of writing a lot of Java code or SQL queries, you can just drag and drop things. It is all very pictorial. It easily tells you where the job is failing. So, you can just go quickly and figure out why it is happening and then fix it."
"The loading mechanisms and administration processes, particularly in setting up connections and deploying the system, need improvement."
"In Syniti Data Quality, data extraction is an area with certain shortcomings where improvements are required."
"The customization of the data needs improvement. We need to build basic SQL queries rather than being able to do it within the tool. We need to be able to analyze the SQL queries and then rerun them based on customer usage."
"It would be good if Syniti Data Quality could integrate more AI in the future."
"Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using."
"If we encounter issues, it’s most likely when using the Talend Open Studio. The studio can be slow, get stuck, or crash. But again, it can be caused by the resources of your machine or your connection with the repository. If we encounter issues with the Studio we restart the Studio. In emergencies, we create and use a new workspace."
"Needs integrated data governance in terms of dictionaries, glossaries, data lineage, and impact analysis. It also needs operationalization of meta-data."
"There are too many functions which could be streamlined."
"When we upgraded to Version 6.4.1, we tried using a GIT repository instead of a SVN repository. After a few incidents where things disappeared and changes were not saved, we decided to go back to a SVN repository."
"The performance is one area that Talend Data Quality could improve in because large volumes take a lot of time."
"There are more functions in a non-streamlined manner, which could be refined to arrive at a better off-the-shelf functions."
"In terms of the solution's technical support, the interactions were satisfactory, but there is room for improvement, especially in managing expectations."
Syniti Data Quality is ranked 6th in Data Quality with 4 reviews while Talend Data Quality is ranked 4th in Data Quality with 20 reviews. Syniti Data Quality is rated 8.6, while Talend Data Quality is rated 8.0. The top reviewer of Syniti Data Quality writes "A highly stable solution that can be deployed very quickly and easily". On the other hand, the top reviewer of Talend Data Quality writes "Saves a lot of time, good ROI, seamless integration with different databases, and stable". Syniti Data Quality is most compared with SAP Data Services, whereas Talend Data Quality is most compared with Ataccama DQ Analyzer, Informatica Data Quality, Alteryx, Precisely Trillium and Ataccama ONE Platform. See our Syniti Data Quality vs. Talend Data Quality report.
See our list of best Data Quality vendors.
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