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.

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.

The effectiveness of the Talend Data Management Platform varies depending on specific use cases. For instance, it integrates customer data from CRM systems, ensuring cleanliness. Additionally, it facilitates the inclusion of accounting information, thereby connecting both datasets seamlessly

The solution is connected to various phones. It retrieves the configuration from the system.

The solution's stability could be improved.

I have used the Talend Data Management Platform for ten years.

I rate the solution’s stability an eight out of ten.

Ten people are using this product. The solution is scalable. It depends on how much scalability and agility you want in the system. Scaling is much easier with added security. I rate the solution’s scalability a nine out of ten.

For deployment, the support was invaluable. A team of 25 members collaborated closely with the tech team to assist them in decision-making. It took a week to deploy the solution.

The product is cost-effective. If you have to do it with Talend or any other informational tools, it's very expensive. It has a very flexible licensing cost. It's a SaaS-based model. It costs around 20,000 bucks. Compared to other ETL tools, it's much more cost-effective and flexible. It's tailored to the customer's needs. They also have data management and solid data suites within their system, not just file management.

The APM is similar, and they have certain tools in Talend, such as scheduling, that provide real-time data. Talend can be complex, but it presents a challenge. The version is much simpler and easier to use. Additionally, it allows non-technical users to access the system, saving resource costs and reducing time wasted on the data platform.

Each one of them has their particular functions. Informatica and Talend have their specific details, which are good. The current UI of Talend and interfaces is good. It's priced at 30,000. Even though it is a startup, Talend's data suite is more promising and they are more agile.

Dell Data Suite is a part of the package that provides compliance and establishing a relationship.

The file management system is part of it. If you want to use it entirely, that's a different package. If you're looking at ETL, you can use the file management system as a staging area, where the cost is a little higher on the second side. More focus is on data transformation, the overall process, and compliance. Other ETL tools cost between fifteen and twenty thousand dollars, but Talend's data suite is a fraction of that price.

Overall, I rate the solution an eight 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.

We recently deployed it for one of our clients, who use it to enhance the quality of their government-related customer data. The primary focus is on ensuring compliance with government policies, and it serves as a crucial component in achieving data quality improvements.

The primary advantage revolves around enhancing the quality of the customer's technology through the utilization of Talend Data Quality. By initiating the process with the tool, users can identify and address various data issues through profiling. This proactive approach results in an improvement in data quality, ultimately contributing to more informed and effective decision-making.

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.

Talend suite might have a missing product, particularly in the commercial master aspect. This would contribute to completing the overall picture, though the focus isn't necessarily on economic considerations. It would be beneficial to have added a greater openness in the tool, allowing for the presentation of data quality results in alternative tools, which would provide increased flexibility in sharing and utilizing data quality outcomes.

I have been working with it for two years.

It provides good stability capabilities.

We haven't applied scalability to any existing customer implementations so far.

In terms of the solution's technical support, the interactions were satisfactory, but there is room for improvement, especially in managing expectations. During recent interactions, there was a sense that the support provided fell short of expectations. The support team communicated that a paid service was available for installation and configuration, but other support needs were not adequately addressed. While there is an understanding of the limitations, better assistance could have been provided. On a scale of one to ten, I would rate the support experience at a six.

The initial setup proved to be challenging for our team. The challenges were more pronounced when deviating from the default setup, especially when opting for a database other than Postgres. The manual installation process appeared less streamlined, leaving room for improvement in its execution. I remember the team investing at least three to four days in the installation process.

For a relatively straightforward scenario, where a single customer addresses Data Quality from one source, the deployment process follows a strategic approach. Initially, the strategy involves focusing on one source system, with the deployment executed by customer engineers and the Talend tool. The deployment doesn't require an extensive team initially; it relies on adequate resources for the deployment phase. However, even in this streamlined process, collaboration with the customer's team is crucial. The deployment necessitates involving other team members from the customer side to ensure the tool is effectively utilized. The process involves deploying, training, and initiating the setup with the initial system. Subsequently, the customer is empowered to continue and expand the deployment journey autonomously. The entire process can be concluded within a month, contingent upon the active participation of the customer team. However, the timeline isn't solely contingent on technical implementation; a significant factor is the adoption on the customer side. Realistically, substantial results become more apparent between three to six months, a duration influenced by factors such as the size of the customer and the complexity of their processes.

The key to success lies in the adoption of the solution within the customer's processes and services. My recommendation is to initiate the implementation by focusing on critical data. By starting with essential data sets, you can swiftly demonstrate tangible results to the business. This approach is strategic because, often, the technical aspects of the technology are not easily comprehensible to the business stakeholders. Begin with a small yet high-value segment to enhance data quality, and then gradually extend the implementation to cover the entire organization. This phased approach ensures a smoother transition and a more significant impact on overall business processes. Overall, I would rate it eight out of ten.

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.

The solution is based on Java. It connects to all available data sources, like APIs, Workday, Salesforce, relational database management systems, Azure, Google Cloud, and AWS. It can do big data processing. It also does batch processing and streaming. It hosts APIs, too. We can consume the queuing mechanisms like Kafka or Java Message Queue.

We migrated from JD Edwards to SAP. It could have been a very tough transition. However, with the help of Talend, we could do it smoothly. It provided us with connectors that made our job easy.

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.

The vendor successfully created a cloud solution. However, lifting and shifting all the functionality available on-premises will be a heavy task. They are working on it. The product must enhance the data quality. Data lineage features must be integrated seamlessly into the data integration platform.

I have been using the solution for more than eight years.

I rate the tool’s stability a nine out of ten.

I rate the tool’s scalability a ten out of ten. We can scale it however we want. It's very flexible.

We used DataStage and Informatica. We switched to Talend because of its price. It also has a wide range of functionality bundled together. While using Informatica, we had to buy everything separately. DataStage and Informatica charge premium prices. It costs us around 25,000 per license.

The solution is deployed on the cloud. The initial setup is fairly straightforward and self-explanatory.

We have seen a return on investment. The solution is easy to deploy. We can deploy it on the JVM servers. We don't need any specific servers to execute the pipelines. People could save a lot of money if they use the tool.

The tool is cheap.

We evaluated Airflow. Talend was a better choice.

It's a fairly self-explanatory tool. We get a lot of advice from the community. People must register with the community. It will help them get started very quickly. Overall, I rate the product a nine out of ten.