Compare Talend Data Quality vs. WinPure Data Quality

Talend Data Quality is ranked 2nd in Data Scrubbing Software with 9 reviews while WinPure Data Quality is ranked 7th in Data Scrubbing Software with 1 review. Talend Data Quality is rated 8.2, while WinPure Data Quality is rated 9.0. The top reviewer of Talend Data Quality writes "Visual jobs are easier to understand than a piece of Java code and improve collaboration between colleagues". On the other hand, the top reviewer of WinPure Data Quality writes "A solution that offers good performance, good stability, and a straightforward setup". Talend Data Quality is most compared with Alteryx, Ataccama DQ Analyzer and Melissa Data Quality, whereas WinPure Data Quality is most compared with Melissa Data Quality and Alteryx.
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Most Helpful Review
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Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
With its frequency function, we were able to pick a line of business to be addressed first in one of our conversion projects.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.The Studio is easy to understand.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.It’s easy to monitor the processes. Every morning I’ll open the Talend Administration Center to check the status of the process. Within seconds I’m able to see which process ran successfully and which have failed and why they failed.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.The numerous components provided by Talend mean you’re able to create jobs quickly and efficiently.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.

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With regards to choosing flash versus traditional storage, it's just so much simpler to manage and users get so much better performance from flash.

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Cons
You can't join more than two tables for analysis.SQL for displaying underlying data in non-match results does not work.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.Needs integrated data governance in terms of dictionaries, glossaries, data lineage, and impact analysis. It also needs operationalization of meta-data.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.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.If the SQL input controls could dynamically determine the schema-based on the SQL alone, it would simplify the steps of having to use a manually created and saved schema for use in the TMap for the Postgres and Redshift components. This would make things even easier.NullPointerExceptions are going to be the death of me and are a big reason for our transition away from Talend. One day, it is fine with a 1000 blank rows, then the next day, it will find one blank cell and it breaks down.

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The pricing model could use improvement.

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Pricing and Cost Advice
We did not purchase a separate license for DQ. It is part of our data platform suite, and I believe it is well-priced.I would advise to first take a look and at the Open Studio edition. Figure out what you need and purchase the appropriate license.

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Overview
The data quality tools in Talend Open Studio for Data Quality enable you to quickly take the first big step towards better data quality for your organization: getting a clear picture of your current data quality. Without having to write any code, you can perform data quality analysis tasks ranging from simple statistical profiling, to analysis of text fields and numeric fields, to validation against standard patterns (email address syntax, credit card number formats) or custom patterns of your own creation.Winpure Data Cleaning Matrix provides a method of applying a whole host of cleaning processes onto your data. Containing an array of tools to help clean, correct, standardize and transform your data the matrix is divided into 7 sections, each section focusing on a specific data cleaning operation. All settings can then be stored and used on other similar data sets thus saving a lot of time.
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Sample Customers
Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset GroupBirmingham Hippodrome, Luton Borough Council, Apex Innovations, Sandia National Laboratories, Clear Blue, Vodafone
Find out what your peers are saying about Melissa Data Quality vs. Talend Data Quality and other solutions. Updated: November 2019.
383,162 professionals have used our research since 2012.
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