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.

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Its greatest asset lies in its user-friendly interface, specifically within the Talend Open Studio, known for its ease of use and familiarity among users. The robust community support proves invaluable when encountering challenges, providing a reliable resource for issue resolution. Moreover, the pricing structure stands out as highly competitive compared to other offerings in the market, making it a cost-effective choice for users. The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work. This functionality enables a streamlined process for identifying, assigning, and subsequently addressing data quality issues.

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I like the components provided by Data Quality, such as:

• Address standardization
• Fuzzy match
• Schema compliance check as they pack lot of code, which is required to perform these standard data operations. 
• Doing the same by coding would be erroneous, take a lot of time, and provide output quality which is biased. 

Apart from specific components, I like idea of storing the results of Data Quality jobs in a DB and having the ability to run reports in the DB to show a dashboard of quality metrics.

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The most valuable feature of Talend Data Quality is its ability to provide six important data quality metrics, such as timeliness and discrepancies. It also offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues.

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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.

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Data ingestion

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The features that I find to be the most valuable are the extensibility, the integration, and the ease of integration with multiple platforms. The integration is one of the great features,  which is mainly what we use it for.

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The numerous components provided by Talend. With these components you’re able to create jobs quickly and efficiently.

I also 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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The ease of transforming data with inputs to TMaps and tJavaRow makes life so easy.

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The option to start with the community edition View full review »

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.

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Maybe the best thing is the product's easy start-up level when you are familiar with Java. Also job creation is fast compared to some other tools. One more good thing is that tables' metadata is easy to bring into the tool and utilize. Last thing to mention here is flexibility to use Java code inside the job.

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JRules, TMap, TParallel, ELT, etc

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Currently the best open source data quality tool available as compared to other open DQ tools ('DataCleaner', 'Open Source Data Quality & Profiling') for of a variety of reasons:

1. Vast connectors to different DB, Web, CRM, etc
2. Custom code is allowed
3. Wide range of advanced algorithms
4. Recommended for advanced users
5. Detailed analysis, etc
6. Large community of users

The most valuable features for us are: custom code, connectors, algorithms.

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The solution is customizable.

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• Analysing data trends: This works when you add a column to analyse. It shows you max, min, nulls, etc. per field. It allows a snapshot of your data.
• Duplication

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• The file fetch process is impeccable. 
• We are able to get emails from URLs very easily using this function when others fail. 
• tLogRows are also great for finding bad data.

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Better control and flexibility to add/custom define features, to tailor to your needs by modifying its Java generated code. View full review »

The ability to build the interface using clear components and access the code (Java) to validate and trace any error. The wide range of components which suits a variety of purposes and provides a flexible development environment to the coder.

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Fuzzy matching lookups.

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