The best features of Talend Data Integration are its rich set of components that let you connect to almost any data design intuitive and its strong automation and scheduling capabilities. The TMap component is especially valuable because it allows flexible transformation, joins, and filtering in a single place. I also rely a lot on context variables to manage different environments like Dev, Test, and production, without changing the code. The error handling and logging tools are very helpful for monitoring and troubleshooting, which makes the workflow more reliable.

Talend Data Integration has helped our company by automating and standardizing data processes. Before, many of these tasks were done manually, which took more time and often led to errors. With Talend Data Integration, we built automated pipelines that extract, clean, and load data consistently. This not only saves hours of manual effort, but also improves the accuracy and reliability of data. As a result, business teams had faster access to trustworthy information for reporting and decision making, which directly improved efficiency and productivity.

Talend Data Integration has had a measurable impact on our organization. By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month. We also improved data accuracy. Error rates dropped by more than 70% because validation rules were built into the jobs. In addition, reporting teams now receive fresh data at least 50% faster, which means they can make decisions earlier and with more confidence. Overall, Talend Data Integration has increased both efficiency and reliability in our data workflows.

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The best feature of Talend Data Integration is its multiple data DB components; we have almost all the components and also cloud versions, with TMC allowing us to perform data preparation and data stewardship.

When going into data preparation with Talend Data Integration, we have scenarios where we use CSV and Excel files to prepare data and for reference purposes. For example, whenever files change, I need to verify manually if the data is correct or incorrect before uploading the data into tables. When preparing the data set, I teach the system what data we are receiving and how we want to convert it, so whenever data comes in, I use the data preparation component to convert it into the desired format before loading it into tables.

We use Data Stewards in Talend Data Integration to verify if the data is correct or incorrect, and business analysts can easily access TMC without needing extensive knowledge of Talend.

Talend Data Integration has positively impacted my organization because it is used for master management data, which I think most other components lack, but Talend Data Integration has that capability for using master management data as standard data.

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The best features of Qlik Talend Cloud are its connectors to many systems, databases, APIs, and files, including both SQL and NoSQL databases. The scheduling feature allows me to run jobs automatically in the cloud, and the built-in data quality tools help clean and check data. The reusable components enable me to reuse jobs and parts, which saves time, making it easy to build good data pipelines.

The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work. This saves time, reduces errors, and makes the pipelines more stable. In my daily work, I can trust that the data is updated on time and ready for reports and analytics.

Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions. This has improved operational stability, productivity, and trust in the data.

Since using Qlik Talend Cloud, we have reduced manual work, resulting in approximately 20% to 30% time savings in data load processes. We have also seen a reduction in operational errors, mainly due to automation and the data quality checks, and a clear improvement in data reliability since the pipelines became more standardized and monitored.

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Talend Data Management Platform hands down is way more simple to use but also quite a bit more powerful in its ability to do multiple things, not just manage APIs and ETL functionality.

The documentation is diamond standard. If you want to know how to do anything, it has been really well documented. If you have a bug or a problem, there is a knowledge database of how to resolve it. It is phenomenal. That was really what set it apart from Boomi to begin with. The documentation and the ease to find out how to do things is phenomenal. The ability of when you build an API and something is not working, it tells you what is wrong with your code. You have not written code, you have dragged and dropped, but it will tell you where you have done something wrong.

From the collaborative point of view, being able to share, we have got a shared server environment, so it means that we can do an update, patch update once. Everybody that is working in that environment gets the same version of the patches. We do not have to worry about people running on old versions. The ability for branches and merging and all of that developer type functionality is really easy, allowing multiple developers to actually work on the same code but not actually stand on each other's feet. Then allowing the business to visually see what we are actually doing.

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Flexibility is a key feature I appreciate about Talend Data Integration, especially the integration of Java within it and the ease of integrating with multiple source repositories such as GitHub and Bitbucket. The latest version of components, release 202503, offers a very broad range which provides access to newer data platforms, and it is relatively inexpensive compared to other tools. I am using it because my client is using it; otherwise, we have many other options in the market.

The collaborative development environment is standard as we follow all the recommended development standards in the development life cycle. For collaboration, we conduct peer reviews for every piece of code, and after peer reviews, we deploy the code in a source repository such as Bitbucket, managing pull requests and merging into the master branch. It is a standardized method of development, and regarding collaboration related to Talend Data Integration, it is normal.

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The process of upgrading the software is quite easy. For instance, upgrading the Studio is straightforward, especially if done automatically. However, I typically do it manually by downloading the patch and placing it in the dedicated folder before restarting the studio. 

I have successfully requested the necessary patch from Talend support multiple times to align different studio versions across the team of developers. It was not difficult, and having access to Talend support directly from TNC proved quite beneficial. Our business has been running smoothly for three and a half years.

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Some of the algorithms that are inbuilt in Talend Data Quality, such as Levenshtein, are the most valuable functions for us. It is helpful for identifying data anomalies with data profiling features.

We perform profiling prior to data quality and post-data quality, and based on that, we determine how much it has improved to measure the efficiency of Talend Data Quality cleaning tools.

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The best features Qlik Talend Cloud offers include the fact that it is built on Java, which gives me the chance to customize my requirements and write my own Java code to achieve my logic.

Qlik Talend Cloud has impacted my organization by increasing the license cost, leading many companies to decide to decommission Talend, as they find the cost too high.

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The solution can run on any machine and that is a big advantage. 

The big data component allows us to do VELTPs. 

The solution is packed with features. 

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The tool was mainly used for ETL processes to apply governance rules on data.

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The availability of connectors is great.

The data integrations on offer are quite useful. 

There's a stitch data loader that has proven to be helpful. 

Technical support is helpful.

It has a pretty straightforward setup, depending on the environment. 

Scalability isn't an issue.

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Talend's most valuable feature is its ability to generate code and packages efficiently. This feature streamlines the integration process, saving developers time and effort. Additionally, the stability of the generated code is noteworthy, as it ensures reliability over time. Despite not always producing the most aesthetically pleasing code, it eliminates critical errors, reducing the burden on developers. This stability is particularly crucial in complex integration scenarios, where errors can have significant consequences.

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The solution is very user-friendly and easy to understand. 

The capacity of the monitoring is great. 

It's rich in connectors and other features. 

The product is stable. 

The initial setup is pretty simple. 

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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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The most valuable feature is the data loading and scripting language. Talend graphs are more user-friendly for developing, modifying, and embedding in business logic.

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The most valuable features of the Talend Data Management Platform are the components.

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The product's integration with PostgreSQL and Jira has been helpful for us. Its performance is good. However, we do not use it for large data sets. 

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We can develop our own code if we do not see the functionality we need. We can write our own code and call it in the integration pipeline. It is the greatest feature. Once we build JAR, we can run it anywhere we want. We do not need Talend to run it. We can use any third-party scheduler to schedule the job and do a performance check.

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I like everything about this product, but the biggest thing is the ease of use. I also like the ability to have files between Linux missions.

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In terms of valuable features, when comparing PMC to TAC, TAC is more centered around the user configuration than the project configuration. If you look at the Talend framework as a whole, you need your Talend administration center, you need a Studio and you need job servers. But there are a few things with respect to job rules, profiling, and project creation which are more advanced in PMC versus TAC. When I say more advanced, in the normal Talend administration center you do not have a super admin and you can have multiple admins who can control their own application. Today, if Talend is used by 10 different applications on TAC, one admin can create those projects for those teams and all other elements which are on-premise. There, you can create multiple admin rules where each application can have its own admins and they can take care of their deployment and they can manage their own environments, etc.... But on the on-premise solution you do not have that, which is a disadvantage.

Let's say in an organization there are multiple applications using Talend. If you run on TAC, you have one admin who has to manage all these projects - assigning projects, creating user rules, providing them access to different environments, you have a dev test and fraud environment. All those things have to be managed by a single admin or maybe one or two admins who have access. But in TMC, which is the Talend management console, you can create sub-admins.

One would be a super admin and you can create those sub-admins who take care of all these things. If I have five applications that are using this solution and I am the admin I don't have to understand their use case and how they want to deploy and all those things. I just give the rules to that admin and that admin takes care of everything. The super admin gives it to those application admins and the application admins can take care of their environment, their deployment process, and creating user rules, etc...

Another thing about the cloud solution is that you don't have to worry about any upgrades. On-premise you have to upgrade your TAC and then you have to upgrade your Studio, which is an effort and time consuming. But on cloud there are automated upgrades, where they get automatically pushed. Then with the Studios, you have to just upgrade that. That is a backwards compatibility. For example, if your Studio is on 7.1 and then the remote engine is on 7.3, that is a backward compatibility. But it's advisable to upgrade your Studios as soon as your cloud instances get upgraded.

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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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What I like about the Talend MDM Platform is that it's a good vendor diagnostic tool.

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The transformations are built in and come as a package for us to drag and drop and complete in one go. The main platform of this tool is Java and if you know how to use Java, it will be a very easy tool to use.

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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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I have been using very basic features, but one of the features that I liked the most is how it handles different contexts. I like the way that you can use the context variables, and how you can work those context variables to give you values and settings for every development environment, such as PROD, TEST, and DEV.

The objects are friendly to use, and another feature that I like is the general generic schemas, even though it took a certain amount of learning to get used to it, it was beneficial.

With what I have seen in the cloud version the interface is great, and what I have used of the on-premises version is great.

It's user friendly.

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The features that I like the most are the simplicity of the interface, and the ability to quickly develop with a predefined component.

Having the ability to mix both my own code created in Java with the predefined components is helpful.

It is easy to face issues and meet any of the client's needs.

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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 most valuable feature is integration. There are several connectors, there is a large community, and the solution updates often. It is always up-to-date.

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As far as the features that I have found most valuable, it's fairly easy to understand, even for someone inexperienced going from Informatica and DataStage to Talend. The other positive thing, although I haven't used it personally, is that it shares the same platform as the data quality solution which is important in my line of work. Both sections are beginning to be asked for by our clients. Unlike a few years ago, when it was either you doing a data integration or somebody else doing a data quality assessment, in this day and age they're actually asking for both, and it is a good platform to do both. The other thing about Talend is that it has a lot of push down features, wherein most of the execution that has to be done on the processing side, does not require much heavy lifting up. There's not much need to lift the data so that it can be processed and then pushed back to the database, which was traditionally the approach for the likes of Informatica and even DataStage if they're not using pushdown authentication.

I put Talend on the same playing field as the other platforms and they're very competitive in terms of performance, which is a good selling point. It has very rich features. It provides a very rich feature set in the application.

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The scheduling part of the solution is very good.  

Talend has a lot of connectors, which is very useful.

The user interface is good. It's very visual.

The basic tools are easy to pick up and understand.

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The most valuable feature is the Talend Admin Console (TAC). It connects Studio to SVN (Code Repository), Nexus (Artifacts/Build Packages), and Job Servers (Runs Nexus packaged jobs). TAC also manages user access to projects, which allows multiple users, working within the same project at the same time, while maintaining all code changes in SVN. 

Talend Studio has the ability to connect to almost anything to integrate data from files, databases, web services, etc.

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It is a very powerful solution to manage historical data and this is one of the key features that I love the most.

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I love the fact that I can copy a step, click a button, and now I have the opportunity to create the opposite of what I was doing. 

For example: If I am creating an inner join which produces a file on the target, there could be records that do not have a match. In other ETL tools, you would have to create a whole different process. In Talend, all you have to do is copy the inner join, and there is an option for rejects. Now you can create a separate file for your business users to review.

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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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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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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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Data ingestion

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

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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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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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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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• 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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Extract, Transform, and Load (ETL) transformations are open source and is a valuable feature.

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Fuzzy matching lookups.

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

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

• It meets our operational and analytical data integration needs by providing us with a high performing set of tools to access, transform and integrate data in real time. • Provides us a central repository for sharing our enterprise metadata among business users, developers and IT staff, so we can access and share a single version of information. • It helped us in handling huge data volumes, remote execution of jobs, interacting with cloud data sources and deployment on cloud infrastructure. • We can centrally supervise our software integration processes by using Talend's advanced monitoring capabilities. View full review »

The solution is customizable.

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Talend Data integration has a wide library of connectors. 

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