Qlik Talend Cloud Reviews

We use Talend for the ETL tool data integration. We're using Talend with Spark to capture large volumes of data from multiple sources and load it to the data lake. We're using version 12.

We deploy it in our organization and to our client through AWS as a cloud provider.

We have one IT consultant and 180 people in different technologies, particularly in the data integration side. At least 50% of them know Talend, and some of them know Ab Initio or Informatica.

We may increase usage, but there are more tools coming up in this space, like Kafka real-time integration. Other data integration capabilities are coming up.

This solution has helped us extract data from various sources and load it into the data lake for our client as a large regional bank.

The most valuable feature is the data loading and scripting language. Talend graphs are more user-friendly for developing, modifying, and embedding in business logic.

There are some glitches, but mostly they'll fix the ongoing feedback we give to the product guys. However, there could be more enhancements. They're bringing deduplication capabilities to Talend. 

I think they should drive toward AI and machine learning. They could include a machine-learning algorithm for the deduplication.

5 Years

It's stable.

It's scalable.

Technical support is average. I would rate them 3.5 out of 5.

Positive

IBM DatStage, Informatica, and Ab Initio, modern technology is the reason I switched

Installation is straightforward. Deployment only takes a couple of days – maybe a day or two with the infrastructure side. For implementation, you have to write a code in order to get the data.

Deployment can be done in-house if you have dealt with a lot of ETL tools that are integration tools.

50% savings in data integration capabilities

The price is on a per-user basis. It's a little more expensive than other tools. There aren't any additional costs beyond the standard licensing fee.

The licensing compared with IBM tools is like 50%.

We evaluated Informatica and Ab Initio. We chose Talend because of the data capabilities. It's a better tool for our requirements.

I would rate this solution 8 out of 10.

We are using the Talend Data Management Platform development.

The solution can be deployed on the cloud or on-premise.

It is easy to create job opportunities within an organization by utilizing data from external sources, which enriches the company's data when connecting to other platforms. Similarly, importing data into your company is a straightforward process. Compared to other solutions, the Talend Data Management Platform makes developing jobs easier.

The most valuable features of the Talend Data Management Platform are the components.

The sales and market department could improve the Talend Data Management Platform.

I have been using the Talend Data Management Platform for approximately six years.

The stability of the solution is excellent.

The scalability of the Talend Data Management Platform is good. It has load balancing and failover features.

We do not have plans to use this solution this year but next year we will increase our usage.

We have four people who use the solution.

Local support from the Talend Data Management Platform can be in Portuguese and sometimes they do not have a resolution. The support can be in different countries but we have a resolution most of the time in Brazil.

The initial setup of the Talend Data Management Platform is of a medium difficulty level.

After two years we break even for the costs of the solution.

The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive.

This solution delivers quick development.

I rate Talend Data Management Platform 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.

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 Cloud Integration was around when a few of our teams wanted to use the remote engine concept on AWS, and with the on-premise we could not do that. They wanted to use Talend on AWS, where they wanted to spin up their own job servers and to process their own data on the cloud, on their own BPC's. They felt that the transitions happening between on-premise to cloud would be more expensive than if they did it from cloud to cloud or from their VPC to remote engines, building up remote engines. So one reason we switched was cost and the second was that there is no dependency if the job servers go down on on-premise and the business could get impacted. Here, there is none of that. It was a kind of seamless integration between cloud to cloud environments and all those things.

In terms of valuable features, when comparing PMC to TAC, TAC is more centered around the user configuration than the project configuration. If you look at the Talend framework as a whole, you need your Talend administration center, you need a Studio and you need job servers. But there are a few things with respect to job rules, profiling, and project creation which are more advanced in PMC versus TAC. When I say more advanced, in the normal Talend administration center you do not have a super admin and you can have multiple admins who can control their own application. Today, if Talend is used by 10 different applications on TAC, one admin can create those projects for those teams and all other elements which are on-premise. There, you can create multiple admin rules where each application can have its own admins and they can take care of their deployment and they can manage their own environments, etc.... But on the on-premise solution you do not have that, which is a disadvantage.

Let's say in an organization there are multiple applications using Talend. If you run on TAC, you have one admin who has to manage all these projects - assigning projects, creating user rules, providing them access to different environments, you have a dev test and fraud environment. All those things have to be managed by a single admin or maybe one or two admins who have access. But in TMC, which is the Talend management console, you can create sub-admins.

One would be a super admin and you can create those sub-admins who take care of all these things. If I have five applications that are using this solution and I am the admin I don't have to understand their use case and how they want to deploy and all those things. I just give the rules to that admin and that admin takes care of everything. The super admin gives it to those application admins and the application admins can take care of their environment, their deployment process, and creating user rules, etc...

Another thing about the cloud solution is that you don't have to worry about any upgrades. On-premise you have to upgrade your TAC and then you have to upgrade your Studio, which is an effort and time consuming. But on cloud there are automated upgrades, where they get automatically pushed. Then with the Studios, you have to just upgrade that. That is a backwards compatibility. For example, if your Studio is on 7.1 and then the remote engine is on 7.3, that is a backward compatibility. But it's advisable to upgrade your Studios as soon as your cloud instances get upgraded.

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.

I have been working with Talend Cloud Integration for about 3 years.

I haven't seen any issues with stability, it's pretty stable.

The scalability depends upon how many you have. If you are looking with respect to performance, the scalability can be whether your job servers are always available, then it is scalable. But if you're looking with respect to performance and you need more job servers, you have to strategize it in a way where you get more tokens to spin up more remote engines.

In terms of pricing, they have the concept of tokens. They give you a certain number of tokens per license. If you have less tokens, then you will have less servers for execution, and that also ties back to the number of licensed servers that you have procured. But if you want more tokens for the remote engines, I think it is around $2000 for maybe 10,000 tokens or a hundred tokens, I don't remember so don't quote me here, you have to pay more to acquire more tokens.

My advice for anyone implementing this solution with respect to the remote engine and TMC connections is that it all depends upon the enterprise. Whether they have single VPC's where they scale up or multiple VPC's where they want to spin up. The cost increases or decreases based upon the strategy. So if I have my remote engine set up on-premise and I want to run the jobs from cloud, the cost will vary, and if you have the VPC to VPC connection then your cost will remain the same. With respect to reliability and with respect to scalability, if you need more job servers, let's say you are trying to process some terabytes or gigabytes or petabytes of data, and if you have two or three Talend licenses on cloud, then I would recommend buying more tokens so they can spin up more remote engines.

The tokenization and the detokenization also apply with this solution like in the 7.3.

I would rate Talend Cloud Integration an eight. The reason is that there are no concurrent licenses, they only have seat licenses on cloud. That's the whole challenge. For example, if in any project your headcount increases or decreases, you do not have that concurrence and you have a seat license, you run into challenges because you have to procure a few more licenses for getting the job done. That's why I would rate them at eight out of 10.

I'm very passionate about this solution because if you look at any other tool that costs around $200 - $300,000, like Delphix which costs you a million dollars, Talend is very cheap and is almost is at par with what others can do. There is one thing which Delphix does which Talend cannot do, but overall, I would say apart from that, if you're looking for a solution, you should give it a try.

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.

Neutral

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 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.

Positive

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.

I use the Talend MDM Platform for managing the master data around ERP systems and customer care systems for clients.

What I like about the Talend MDM Platform is that it's a good vendor diagnostic tool.

What's missing in the Talend MDM Platform is that it's not maintaining technology references. For example, my company needs a reference case if the platform has been implemented for a configuration that's similar to the client's required configuration. Currently, the client is still reluctant to roll out the Talend MDM Platform at a wider level because there's still no reference received from the Talend team.

I've been using the Talend MDM Platform for one year.

Talend MDM Platform has very good stability.

We still haven't scaled the Talend MDM Platform because we're still waiting for a reliable reference for configuration.

My experience with the technical support team of Talend MDM Platform was good. The team was responsive and solved my issues. On a scale of one to five, where one is the worst and five is the best, I'm giving support a three.

Talend MDM Platform is the only solution my company uses.

The initial setup for Talend MDM Platform was straightforward. It took three to four months to deploy it the first time.

We used a third-party to deploy Talend MDM Platform.

The licensing cost for the Talend MDM Platform is paid yearly, but I'm unable to give you the figure. I would rate its price as four out of five because it's on the cheaper side. I'm not aware of any extra costs in addition to the standard licensing fees for the Talend MDM Platform.

There were no other tools evaluated apart from Talend MDM Platform.

More than ten people use the Talend MDM Platform within the company. Three to four people take care of the deployment and maintenance of the platform. Until the Talend team gives a reliable reference model for configuration, my company is stuck and can't increase the visibility or usage of the Talend MDM Platform.

Talend MDM Platform is a good solution, so I'm rating it seven out of ten.

My advice to anyone looking into using the Talend MDM Platform is to look for existing references for it because right now, what's missing in the solution is the technological reference.