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.

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.

Talend Data Quality helps me find and fix problems in my data. It checks for errors and follows rules to ensure my data is accurate. If it finds issues, it works together with me and the data stewards to fix them. It is like a team effort to make sure my data is good quality from the start.

Talend Data Quality made a big difference for our organization. For example, when we were switching from one system to another, and the data in both systems was different, with different rules. When we were moving the data, it was crucial that the quality remained high. Talend Data Quality helped us ensure that the data was accurate and consistent during this transition.

The most valuable feature of Talend Data Quality is its ability to provide six important data quality metrics, such as timeliness and discrepancies. It also offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues.

In terms of improvement, Talend Data Quality needs better dashboarding. Currently, it provides static PDF reports, which are not very dynamic. It would be more helpful if it offered dynamic dashboards that could be directly used by clients for better analysis.

I have been using Talend Data Quality for over seven years.

I would rate the stability an eight out of ten.

I would rate the scalability an eight out of ten.

Technical support is good. I would give it a nine out of ten.

I have previously used Informatica Data Quality. I switched to Talend Data Quality because it offers more flexibility. It has better connectivity options, including the ability to connect with Apache Hadoop and big data environments, which Informatica couldn't provide.

Deploying Talend Data Quality is not complex. It is based on Java, so once you build it, you can run it easily in various environments. It is that simple.

There are many data quality tools available, but some can be expensive. Talend Data Quality stands out because it is often provided for free if you already have Talend Data Integration, which means you don't need to buy new licenses.

My advice to new users is that if you are looking to identify data issues, I would recommend Talend Data Quality as a cost-effective and efficient choice. However, if you also want to enforce rules and handle discrepancies, Talend Data Quality may not be enough, and you should consider Talend's integration solutions for a more comprehensive approach. Overall, I would rate the solution an eight out of ten.