We performed a comparison between Informatica Cloud 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."Stability-wise, I rate the solution a ten out of ten."
"Initial setup was fairly easy."
"The user-friendliness and performance of Informatica is quite impressive."
"The most valuable feature is the rule specification."
"The most beneficial feature of Informatica Cloud Data Quality is it's cloud-based."
"The profiling features are much better than the on-premise version."
"One of the most valuable features of Informatica Cloud Data Quality is Master Data Management. You can write code to build your logic rules to check the quality."
"We primarily used Cloud Data Profiling to connect with Cloud Data Governance, a tool also used by Teva. This integration allowed users to access data quality results within the data governance catalog."
"It offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues."
"It lowers the amount of time in development from weeks to a day."
"The file fetch process is impeccable."
"The solution is customizable."
"We have used value frequency and patterns. We have been it impressed with these functions as they have helped us in making decisions in transformation work."
"We are able to get emails from URLs very easily using this function when others fail."
"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 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."
"In the on-premises version, features like web service consumer and web service provider are available, but these functionalities are currently unavailable in the cloud edition."
"Some capabilities from the cloud version are not included in the on-premises version."
"Logical views are a little bit behind in comparison to the on-premise version."
"If given the opportunity, I would like to address these concerns, particularly with regard to enhancing the end-user experience."
"Informatica Cloud Data Quality could improve by adding more algorithms for matching and mastering. We currently only have five or six. Additionally, the parallelism in data is better in other solutions, such as IBM."
"I used to use this tool more but recently we have been using another tool that we feel is better because it handles spatial data. Informatica Cloud Data Quality could improve by adding the ability to handle spatial data."
"The high price of the product is an area of concern where improvements are required."
"The integration with older technology and cloud quality needs improvement."
"The ability to change the code when debugging the JavaScript could be improved."
"I would say that some of the support elements need improvement."
"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."
"The performance is one area that Talend Data Quality could improve in because large volumes take a lot of time."
"You can't join more than two tables for analysis."
"There are too many functions which could be streamlined."
"In terms of the solution's technical support, the interactions were satisfactory, but there is room for improvement, especially in managing expectations."
"In redundancy analysis, the query is failing to bring non-matched records. This query is an internal script. There is no way (that I know of) to fix this syntax error for future runs."
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Informatica Cloud Data Quality is ranked 3rd in Data Quality with 8 reviews while Talend Data Quality is ranked 4th in Data Quality with 20 reviews. Informatica Cloud Data Quality is rated 8.2, while Talend Data Quality is rated 8.0. The top reviewer of Informatica Cloud Data Quality writes "Is user friendly and can handle large volumes of data without compromising performance". 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". Informatica Cloud Data Quality is most compared with Informatica Data Quality, whereas Talend Data Quality is most compared with Ataccama DQ Analyzer, Informatica Data Quality, Alteryx, Precisely Trillium and Ataccama ONE Platform. See our Informatica Cloud Data Quality vs. Talend Data Quality report.
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We monitor all Data Quality 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.
