In most cases, we use Talend Data Integration for a data integration process, including data migrations from a legacy system to Snowflake as Salesforce site, with most of the usage for ETL purposes.

A specific example of a project where I used Talend Data Integration is when we have legacy systems such as SQL Server, Oracle, and MySQL, where we are extracting data and loading it into the staging area in the Snowflake site. Once it reaches Snowflake, we do some transformation and move the data to a staging area in Snowflake, before publishing the data into the Salesforce site. This is one of the processes we follow for moving data and doing transformations, and during data migrations, we use Talend Data Integration as the ETL tool.

I think it's the main use case; most of the time we use Talend Data Integration for data integration purposes.

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.

Talend Data Integration can be improved by reducing the license cost, as it is a bit high compared to other tools, which can be a burden for small-scale companies wanting to buy a license.

The customer support for Talend Data Integration is good because I connect directly with the Talend support team when facing issues, and there is also a Talend technical architect who supports us.

I am using Talend Data Integration for up to eight years now.

Talend Data Integration is stable, as I have used it for many projects and everything is working fine.

The scalability of Talend Data Integration is good; if it weren't scalable, it wouldn't be reliable. Although I have 13 years of experience with various tools, I have worked on Talend Data Integration alone for 8 years and find it performs well.

The customer support for Talend Data Integration is very good; whenever I raise a ticket in the customer portal, I immediately receive an email, and follow-up communication is prompt.

We have a partnership with Talend for support, and two people assist us, along with a weekly call to address any issues.

We previously used SSAS, Pentaho, and other ETL tools such as Metabase, but after some evaluation, we found Talend Data Integration to be the best option because it offers a relevant package for our needs, including master data management.

Before choosing Talend Data Integration, we planned to move to Informatica.

I have seen a return on investment with Talend Data Integration, as a previous client in the UK saved time by using it. They didn't want to use separate ETL tools for MDM or for TMC and data preparation, which is all included in one package.

My experience with Talend Data Integration's pricing, setup cost, and licensing is that it is a bit higher compared to other tools, making it not very affordable.

I rate Talend Data Integration a 9.5 out of 10.

If others are looking into using Talend Data Integration, my advice is to go with the TMC license along with data preparation and data stewardship, as it will enhance your project's accuracy and data quality while reducing manual interventions.

Talend Data Integration is a great tool, and if they could reduce the license cost, it would be the best tool on the market.

My main use cases for Talend Data Integration are ETL processes and data migration, but also automation of recurring data flows. For example, in one project, I used Talend Data Integration to extract sales data from multiple CSV files, provided daily, clean and normalize the data, and then load it into a SQL Server database. This automated process replaced a manual task, reduced errors, and ensured that the reporting team always had updated data available. In another case, I used Talend Data Integration to migrate data from Oracle to SQL Server, including handling data type conversions and ensuring data quality during the transfer.

Talend Data Integration helped automate recurring data flows. For example, I set up jobs that would automatically extract daily files from an input directory, validate and clean the data, and then load it into our SQL Server database. Once deployed, these jobs run on a schedule without manual intervention. This saved a lot of time because the process used to take several hours manually. It also reduced errors, since the validation rules in Talend Data Integration ensured that only clean and consistent data was loaded. It made the overall workflow much more reliable and efficient.

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.

The user interface of Talend Data Integration could be more simple and intuitive to reduce learning curves. Processing large volumes of data sometimes consumes a lot of resources. Automatic optimization would be very useful. Managing and deploying multiple jobs in complex environments could be easier with stronger versioning tools. Debugging and error tracking could be more visual and faster. It would be great to have more ready-to-use connectors for modern cloud and SaaS platforms. Real-time and streaming integration could be improved to complement batch processing. The documentation could be more detailed with more practical and concrete examples.

I have been using Talend Data Integration for two years, working mainly on ETL and data migration projects.

Overall, Talend Data Integration is stable. Once the jobs are properly designed and deployed, they run reliably without major issues. Performance depends on the volume of data and the infrastructure, but in general, it is very dependable.

Talend Data Integration is quite scalable. It allows us to handle larger data volumes and more complex jobs as our needs grow. By using features like job parallelization and modular design, we can expand our data flows without having to rebuild everything. It can definitely support organizational growth.

Customer support for Talend Data Integration has been helpful. The support team is responsive when we raise issues, and they usually provide clear guidance or solutions. Sometimes, the resolution takes a bit of time for complex cases, but overall, my experience with Talend Data Integration support has been positive.

I have not used any previous solutions.

We have definitely seen a return on investment with Talend Data Integration. It has helped us save a lot of time by automating repetitive data processes and reducing manual interventions. It also reduced errors in our workflows, which means less rework and faster delivery. In the long run, this has saved both time and cost for our organization.

Over the past two years, I have not only learned how to use Talend Data Integration effectively, but I have also developed good practices that make data prior to maintain. I really enjoy working on ETL and data integration projects because they bring real value to the business by making data cleaner and more accessible. I am excited about the possibility of applying these skills in a new environment and continuing to grow my expertise.

I would rate Talend Data Integration 8 out of 10 mainly because of a few areas that could be improved. The user interface can feel complex for beginners, and processing very large data sets sometimes consumes a lot of resources. Also, debugging and error tracking could be more intuitive. These do not prevent us from using it effectively, but they are areas where I see room for improvement.

My advice for others looking into using Talend Data Integration is to invest time in designing jobs in a structured and reusable way. This makes maintenance much easier as projects grow. I would also recommend taking advantage of automation and scheduling features early on, since they save a lot of time. Finally, new users should focus on learning best practices because it really improves performance and stability.

We are not only a customer of Talend Data Integration, but also a partner. This partnership allows us to collaborate more closely with Talend Data Integration and leverage their solution in a more strategic way.

We use it with internal Linux servers for deployment, utilizing Azure DevOps, not AWS.

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.

I haven't worked with Talend Data Integration on real-time data processing; I have only seen the streaming component, and I think it can accommodate limited streaming, not at the level of larger big data platforms.

Regarding ETL, Talend Data Integration is great, but concerning real-time data processing, people are not really sure about Talend Data Integration or might not know how it provides such types of flexibilities. I haven't worked on this aspect in Talend Data Integration, so this is definitely an area where it lacks.

To elaborate on one drawback, if I am designing something in Talend Data Integration, I cannot backtrack in the flow. For example, if I read data from a source and transform it before applying complex aggregation in the third step, I cannot join this data with the second step. If I need to go back, I will have to use a memory component, or if I need to avoid memory consumption, I will have to read the data from step one again to join it with the third step.

I have five or six years of experience with this product.

Until now, I haven't had a chance to contact Talend Data Integration's customer support, as our use cases have not been difficult. For complex tasks that I couldn't accomplish with Talend Data Integration's standard components, I just developed my own language in Java routines. Therefore, I haven't had a chance to engage with their support, so I'm not very sure about it.

I have not used other integration platform-as-a-service solutions such as Boomi or SnapLogic.

I am not familiar with Talend Data Integration's Visual Design Environment.

I do not assess the importance of Talend Data Integration's pre-built connectors for my organization's data integration needs.

This process obviously helps improve data quality.

I do not know the main differences between Talend Data Integration and other data integration products.

The score of 7 out of 10 is due to the limited real-time data processing capabilities mentioned.

Our primary use case involves integrating diverse data sources into our projects. These sources include data servers, fax files, CSV files, databases such as SQL and Postgres, and various other formats like Excel.

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.

The product's setup process could be simpler.

We have been utilizing Talend Data integration for five years.

Our organization utilized Microsoft's SSIS for data integration before. However, we switched to Talend because of its simplicity and ease of use.

One key difference between Microsoft's SSIS and Talend data integration is their approach to integration. Talend emphasizes Java integration, which a robust Java assistant further supports. It provides its job ecosystem, which users utilize.

The initial setup was relatively straightforward, although it required careful attention to various components such as developer training, transformation models, and system configurations. Its versatility extends beyond simple data integration, offering powerful tools for backend processing and Java library integration. Challenges during implementation were mostly related to the complexity of the ecosystem and licensing issues, but these were manageable with appropriate expertise. While the setup may require effort, its robust features make it a valuable asset for organizations seeking efficient data processing solutions.

The product pricing is considered very good, especially compared to other data integration tools in the market.

In a previous role, I implemented Talend's integration solutions, providing installation and training to my team. It was vital for processing data in our databases and BI platform. For instance, it efficiently handled file reception from clients, identifying errors and integrating data seamlessly. This streamlined approach improved efficiency, especially with a substantial increase in processing volumes to 38,000 batches.

I recommend the product to others and rate it a ten out of ten.

We use the product to collect and report data to the Power BI dashboard.

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.

Due to using the open-source version of Talend Data Integration, which lacks a scheduler, our current approach involves developing jobs in Talend, exporting them as Java packages, and utilizing an external scheduler, such as Windows Scheduler, to manage the scheduling process.

I have been using the product for nine months.

The product's stability is good.

We have no support since we use the open-source version.

I have used Informatica before Talend Data Integration.

The tool's deployment time varies based on the development scope. Smaller deliverables may take half a day in an agile environment, while larger deployments could exceed several hours. On average, deploying one job takes around two to three hours. We may require three resources to handle the deployment.

I have been using the open-source version.

The tool's learning curve for new users is good. I rate the product an eight out of ten. Before using Talend Data Integration, it's important to remember that it requires effort in data development. There are numerous configurations to handle, and the deployment process can be lengthy, depending on the specific version in use.

Talend Data integration has a wide library of connectors.

The tool's technical support needs to be better. It doesn't have a local data center but pushes everything to the cloud. They need to check in with customers to see if they're happy and how well the solutions work. They need to assign a customer success manager for the accounts they sell.

I have been using the product for four and a half years.

Talend Data integration's stability is good.

I rate the tools' scalability an eight out of ten.

Talend Data integration's initial deployment is simple.

I rate the product an eight out of ten.