IT Consultant at a tech services company with 201-500 employees
Real User
Top 10
Sep 26, 2025
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 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.
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.
Principal Consultant at a tech consulting company with 1,001-5,000 employees
Reseller
Dec 13, 2023
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.
Data Office Lead at a comms service provider with 501-1,000 employees
Real User
Oct 17, 2022
The license model needs to be overhauled. Most SaaS solutions are moving to consumption, PAYG, or a subscription model against named user based. Currently, the solution is not geared toward data-driven, self-service-friendly orgs.
Developer at a tech services company with 10,001+ employees
Real User
Jul 26, 2022
Sometimes there are bugs which are unidentified and we have to follow-up with the Talend team to resolve them. In a critical situation, it takes time for them to update patches. In a month, we get two or three patches and this means we need to redeploy completed jobs.
IT Manager at a insurance company with 10,001+ employees
Real User
Mar 4, 2021
In terms of what could be improved, there is not much. It's a basic server setup. It all depends upon what kind of software you want to put on that server. With the remote engine you are building an EC2 or you are setting up an EC2 instance and then pushing all your software there and then running it from there. I don't see any challenges with that.
Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite...
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 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.
The product's setup process could be simpler.
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.
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.
The license model needs to be overhauled. Most SaaS solutions are moving to consumption, PAYG, or a subscription model against named user based. Currently, the solution is not geared toward data-driven, self-service-friendly orgs.
Sometimes there are bugs which are unidentified and we have to follow-up with the Talend team to resolve them. In a critical situation, it takes time for them to update patches. In a month, we get two or three patches and this means we need to redeploy completed jobs.
In terms of what could be improved, there is not much. It's a basic server setup. It all depends upon what kind of software you want to put on that server. With the remote engine you are building an EC2 or you are setting up an EC2 instance and then pushing all your software there and then running it from there. I don't see any challenges with that.