My main use case for Qlik Talend Cloud involves using it for data integrations and ETL, ELT pipelines to support our data warehouse and analytics platforms.

A specific example of how I use Qlik Talend Cloud for data integration involves working on several data engineering projects across different industries, with a main focus on data integration, analytics, and cloud. In designing and implementing a complete data warehouse, I integrate multiple data sources such as ERP, e-commerce platforms, and external APIs. In this role, I use Talend extensively to build and manage ETL pipelines, improve performance, and ensure data consistency for BI tools such as Power BI and Grafana. Before that, at Accenture, I worked on large strategy and international projects, supporting and modernizing data platforms for global clients. My experience covers the full data lifecycle, from understanding business requirements and designing the architecture to developing, deploying, and maintaining outstanding data products and great data pipelines.

I want to add that we use Qlik Talend Cloud not just as an ETL tool, but as part of a broader data architecture. I rely on it to create reliable, reusable, and well-governed pipelines, especially in environments where data comes from many sources and needs to be delivered consistently to analytics teams. I pay a lot of attention to performance tuning, job design, and monitoring so pipelines are stable and easy to maintain. I also use Qlik Talend Cloud in standard data models, enforce data quality rules, and support incremental and near-real-time loads when needed. This helps ensure the downstream systems such as data warehouses, dashboards, and compliance reports always receive accurate and trusted data. Overall, Qlik Talend Cloud plays a key role in helping me deliver scalable, production-ready data solutions that align technical implementation with business needs.

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.

Qlik Talend Cloud could be improved with more advanced monitoring and flexible alerts, as well as better job performance visibility.

It would also help to have a better job version and comparison experience to support team collaborations. Additionally, having more detailed error messages and easier job debugging would be beneficial. Better cost and resource visibility would also help teams optimize their workloads.

I have been using Qlik Talend Cloud for 10 years.

Qlik Talend Cloud has not presented any stability issues.

Its scalability is good, as Qlik Talend Cloud can handle large amounts of data and grow as needed, especially in cloud environments.

The customer support for Qlik Talend Cloud is good.

We have seen a return on investment with Qlik Talend Cloud. We achieved around 20% to 30% time savings in the ETL process, reduced operational errors, and improved pipeline stability, allowing my team to focus on analytics work without needing to increase headcount.

My advice to others looking into using Qlik Talend Cloud is to start with simple use cases, use automation and scheduling, and define standards early. This helps deliver value quickly and keep pipelines stable and easy to maintain. I would rate this product an 8 out of 10.

My main use case for Qlik Talend Cloud involves extracting data from different sources and loading it to the target database by applying various transformation rules that I receive from the business.

In a specific project, my role involves extracting data from SAP systems and different databases, applying the transformation rules received from the business, and loading it to target database systems, which could include anything from files to databases. After the data is loaded, I perform some unit testing and load the job into Qlik Talend Cloud.

Regarding my main use case with Qlik Talend Cloud, I feel that compared to other ETL tools, Talend is better because it is built on Java; this allows users to customize their own requirements and logic. It is very easy to use and offers multiple connectors and components, with around 900 plus components available.

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.

To improve Qlik Talend Cloud, I think the license cost should be reduced, and it should collaborate with multiple companies like Salesforce to enhance its performance.

I have been working in my current field for around eight years.

Qlik Talend Cloud is very stable.

The scalability of Qlik Talend Cloud is up to the mark.

The customer support for Qlik Talend Cloud is very good.

My experience with pricing, setup cost, and licensing is rated as one.

I rated my experience with pricing, setup cost, and licensing as one because the license cost has increased significantly, leading many companies to seek more profitable options in the market.

As companies decide to decommission Talend due to the license cost, my team has migrated all the Talend ETL jobs to Java, Spring Boot, and Spark, resulting in many Talend developers leaving the project since there is no work for them to do.

My advice for others looking into using Qlik Talend Cloud is to consider shifting to other cloud technologies like Azure Data Factory or AWS, as there are multiple ETL tools available that they can switch to. I have rated this review as a 7.

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.

It is for consistency, mainly; data consistency and data quality are our main use cases for the product.

Data consistency is the primary purpose we use it for, as we have written rules in Talend Data Quality to ensure whatever data we receive is consistent and fulfilled, so there are no gaps.

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.

I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback.

I may not know what Talend Data Quality can improve for data quality. I'm not well-versed with the latest technology, so I cannot suggest additional functions that could make the product better or simplify my work with it. Generally, whatever jobs we have written in Talend Data Quality are working fine, so I am satisfied with that.

I have been working with Talend Data Quality for a couple of years now.

The stability for Talend Data Quality can be rated around an 8; it is quite stable.

I evaluated scalability in the past but not in recent days, so I don't know if something has changed. It can be rated around a 7 for now, and since I'm still using on-premises, I don't know what has changed in cloud or any other aspects.

I am satisfied with Talend Data Quality technical support.

I would rate the technical support from Talend Data Quality as an 8 or 9.

Informatica is one of the solutions I have used in the past to compare with Talend Data Quality, and I consider it a competitor.

The initial setup for Talend Data Quality is not complex; it is simple.

We completed the deployment process for Talend Data Quality in-house with our team, without requiring any consultant or integrator.

I am still using on-premises, so I don't know how much of the latest version is available. I am satisfied with Talend Data Quality, so I will still continue using it.

Currently, I'm working with batch jobs and don't perform real-time data quality monitoring because of the large data volume. For real-time, we use a different product.

I cannot provide details about the data monitoring products we integrate with Talend Data Quality because it is proprietary information.

I'm not involved in pricing as it's procured by somebody else.

I am satisfied with the performance part, and I think it could be better.

On a scale of 1-10, I rate Talend Data Quality an 8.

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.

At the moment, we are purely focused on building all of our ETL and API environments out. Being a council, we have over 300 databases with almost twice as many external SaaS platforms that we need to integrate. We have only got a tiny team of about three people, and we are quite quickly building that environment out with a 24-month roadmap. We are doing everything from capturing emails out of Microsoft Exchange into our document management system, converting GIS data into PDFs and transferring it through multiple different systems, moving building data from one cloud platform and transforming it into multiple cloud platforms. Everything is very much a move and shift and twist and reshape and make things out of something that was not really there to begin with.

Primarily it is around the integration. We have gone for a sort of hub-and-spoke architecture where we have multiple cloud solutions. They all integrate into our Talend Data Management Platform, and we use the Talend Data Management Platform to integrate into the other cloud platforms, rather than spending money on SaaS solutions. Every time they upgrade, they change their integration, and we have got to pay a lot of money for people to upgrade all the APIs every time they change. So we manage all the integration between all of our different platforms and our databases. It standardizes the data formats.

Pretty much everything is event-driven. When something occurs in one place, multiple different things can occur almost simultaneously with webhooks and all of that sort of functionality. Every time an event occurs in one place, it can cause a number of different jobs and processes to transform that original record into multiple places across our environment.

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.

There are always areas for improvement. They have actually in the last 12 months done a lot of work on the UI. If you had contacted me at Christmas last year, the UI was really dated. Now, they have not finished cleaning it up or bringing it into a new flash version. But because it has such a simple design, it is actually so easy to use. They have taken a lot of the complexity out of it, so you just click on a button and it does it all for you.

I think one weakness, which I might have worked around today, is spatial data. But that might change by Monday. That is again because there is so much functionality, actually the real weakness is they have got too much functionality, if that is a weakness. Because there are 10 ways to do everything, and each way has its own pros and cons, and you really have to make your decision about which way to implement it. When you have five developers or three developers doing something, it is very easy for three people to do the same thing three different ways.

On the flip side, that is one of its amazing strengths, as you are not locked into a very rigid way of doing something. It means that a lot of our custom environments are fantastic because we can just plug it in a slightly different way. But because it does so much, it is a lot to get your head around the whole thing. We tend to focus on the small stuff that we are adding value to right now.

We have been using Talend Data Management Platform for almost two years as of October. I know that because we have got to renew our license. An email came through this morning, so we are about six weeks away from being two years.

In itself, it is really stable. The biggest challenge is around the Java environment with security software that is based on Windows. If you run anything using Java, and it is not just Talend Data Management Platform but any Java type application, there are normally challenges around your security software because security software seems to hate Java for some reason.

Scalability is really simple, you just add another engine and put it into the cluster and it just works. It is phenomenally simple with a really simple UI to do that. So it works really well.

Their AI is fantastic. We logged probably about eight jobs last year for support, and this year we have logged about seven or eight in the last six months as we have had a new person on it. All of the jobs that we have logged this year have been answered by their AI, so we have not actually talked to anybody. When we have had to talk to people in the past, they have been very good. They really know their product.

We looked at Boomi and I think there were six different products in total. This was about two and a half, three years ago. We started off with a very in-house built solution, a .NET solution that was not working and did not really do what we needed it to do. We went through about three other products and ran each product for about three or four months. When I came, they had all the products actually still running. Then we evaluated it and brought in another three solutions. Out of that, Boomi was on top. Talend Data Management Platform was there, but because it was open source, the CIO did not want to go down an open source track. It was not until I started using it at home after we had been using Boomi for about four or five months and it prompted me for my organization name, and then I got promotional materials from it. That is when we found out that it was not just open source, there was a paid version to it. As soon as that happened, we evaluated Talend Data Management Platform, and we had to meet some really strict criteria for it to jump from Boomi. When we could prove that it actually met all of that criteria, then we did a proof of concept and eventually went whole hog with Talend Data Management Platform.

The setup was really easy. You just double click on the executable and it will prompt you for everything.

We did a little bit of analysis on the return on investment and that is really what kept us in the Talend Data Management Platform space because we achieved so much in the first six months. We actually achieved the first 18 months worth of work in the first six months. So for us, it actually was a really good return on investment, but it was a challenge to get the cost approved by our senior executives.

We got a quote for one single API on one of our platforms. That is the equivalent cost of that API to three years worth of support and maintenance from Qlik.

For us in New Zealand, it is quite expensive. In America, I think it would be quite a reasonable price. That is one of the challenges in New Zealand. We do not have a lot of money to throw around.

It is one of those products where you rarely have a salesman tell you that it can do something, and then you put it in and realize that the salesman was actually not telling you the whole truth. In this case, the salesman told us what it could do, and we have unpacked it and it can do way more than what we actually ever intended to. Initially we had a footprint we wanted it to finish. We have done what we originally wanted and we have done so much more. There is so much more that we can do with it that we just have not had time to deal with.

The data matching and cleansing type functionality, we are doing a lot of that because we are dealing with quite large data sets. If you tried to write code, it would just be spending more time debugging it than anything else. So we really leverage a lot of the Qlik toolset where we can. We have not hooked up to the AI yet. That is just a little bit too far in advance of what we are actually doing, our capability at the moment. It would probably be of benefit, but at the moment it is all about getting the data clean from one end to the other. We are getting on top of it a lot faster than what we originally thought we would be doing.

The security on the product is okay. It is the way that Java applications interact with security software that is the challenge. The product itself is fantastic, but security software does not Java applications and Talend Data Management Platform is a Java application.

I rate Talend Data Management Platform a nine out of ten.

We were using Talend as a data warehouse for SecureSkilled Data related to a pension system. It acted as an SQL repository and was used for data warehousing and data governance, applying constraints on data before loading it into the warehouse.

We used Talend for ETL functionalities and to apply governance rules on some fields of data tables in our data warehouse.

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

The biggest challenge with Talend was its performance. Sometimes data cleaning from the data warehouse was very slow, and we had to restart the server to enhance performance.

We used Talend for around two years, from 2021 till 2022.

Sometimes, our data cleaning process was very slow, requiring server restarts.

I did not contact Talend technical support.

I also worked with Informatica, which I found to be better in terms of performance and cloud availability.

I was not involved in the deployment, but it took around two to three weeks.

It was managed internally by our resources.

Talend was an open-source tool, so there were no fees associated with it.

We were comparing Talend with Informatica, which I found to be more performant and provided features like dashboards for data quality.

I recommend Informatica over Talend due to Talend's performance issues.

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.