Qlik Talend Cloud Reviews

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.

Our company uses the solution to provide data integration services for customers. Most of our implementations are on-premises for large or enterprise-level customers. 

We have six users with experience in both the local studio and the enterprise versions. 

The solution can run on any machine and that is a big advantage. 

The big data component allows us to do VELTPs. 

The solution is packed with features. 

There are some limitations on the monitoring side so support is not minimal when compared to Informatica. 

The solution's memory sometimes bottlenecks and that can be challenging, even for technical support. For example, we sometimes have issues with loading 16GB files. 

The logging and scheduling sides could improve. 

I have been using the solution on and off since 2007. 

The stability is rated an eight out of ten. 

The solution has great, dynamic scalability. You just put it on the cloud and run it with no issues. 

Technical support sometimes struggles to solve memory bottlenecks. 

The setup is moderate but not too complex. The implementation is more complex because you have to configure everything.

We implement the solution for customers. The implementation time depends on the client and project. 

A large-scale implementation with different XMLs, different key messaging queues, and big data takes six months to a year. 

Customers realize ROI because the solution saves time and resources. 

The solution's pricing is very reasonable and half the cost of Informatica. 

The solution sometimes has memory bottlenecks when processing big files. Other products such as Informatica load files very easily. 

Informatica is built for ETL and has more functionality tied to that mode, but is twice the price of the solution. 

The solution is built for everything including ELP, ETL, and data or application migrations. 

I recommend the solution because it is customizable and flexible. I rate it a seven 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.

Neutral

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.

We are currently using it for data integration and API application integrations. The integrations are used for sourcing the data from various sources. We're getting it into one data platform.

The availability of connectors is great.

The data integrations on offer are quite useful. 

There's a stitch data loader that has proven to be helpful. 

Technical support is helpful.

It has a pretty straightforward setup, depending on the environment. 

Scalability isn't an issue.

It's lacking on the change data capture side. When we go into projects of that nature, we move over to other vendors to do the job. It would be helpful if they were stronger here. 

I'd be interested in seeing the running of Python programs and transformations from within the studio itself. Right now, we have to call it from the CLI. We'd love to use it in the same way we use Java, with more simple copy-paste functionality.

I've used the solution for six or seven years. 

The stability does depend on the features. Some features are not as stable as they could be, and some are not backward compatible. If there is some mismatch, it can break. You do have to update everything separately, and it is important to update from the same source.

It all comes down to how you set up everything. 

Some features are really stable. Some features are not backward compatible. One of the issues that I am currently facing is making sure that the API pipelines that we have created are version compatible with the run time server that we have installed. Ffrom a readability point of view, if you have to upgrade each and every thing separately, it's hard. There is a little bit of room for improvement when it comes to upgrading from the same source. That said, it depends on how you are setting up your environment.

We currently have the data integration environment and the remote engines and everything set up so that our upgrades happen using just one script. For Runtime, that is not yet done. This is something which we are doing currently. So stability totally depends on the way you configure it. Of course, there is room for improvement from the Talend side as well.

The solution is scalable. It is platform-independent.  Scaling depends on the person providing the infrastructure. If we've provisioned properly, there are no particular limitations. 

In one project, we have 40 developer users and another 15 people in stewardship positions. 

We contact support frequently, and they are great. I haven't had a bad experience yet. 

We have used other vendors. Currently, there are two types of vendors you can see on Gartner: those that are cloud-based and some that are hybrid. Talend is a hybrid tool. As an overall data fabric solution, Talend is leading the market. It used to be Informatica. However, their cloud is not yet that mature. 

When it comes to cloud integrations, Talend has competition from Azure Stack. While not that mature, it is really competing with Talend head-to-head and has a lot of great applications. 

Half of my clients prefer Talend, and the other half go to Azure. 

Depending on how you want to manage the project, it's pretty easy to set up. A simple spin-up is quite fast, and you can use it right away. However, it offers CI/CD pipeline compatibilities, and that process might take longer. 

The pricing is moderate. It's not too high or low. If you want a small setup with five or six users, Talend is a good option and comes quite cheap. It's a great way to enter into this area of technology.

I'd rate the solution eight out of ten. 

We are Talend platinum partners. 

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.

We use the solution in our customer's company. The solution is used for monitoring. 

The solution is very user-friendly and easy to understand. 

The capacity of the monitoring is great. 

It's rich in connectors and other features. 

The product is stable. 

The initial setup is pretty simple. 

The business model needs adjustment. It's based on the connector and the IP you want to connect with. 

There isn't a great difference between this and other products. Globally, there is no big technical difference between this and others. 

We'd like to see more connectors it the future.

The solution is not very transparent. You aren't sure what you are buying or what you need. It's hard to quantify the product.

I've been using the solution for four years. 

It is stable and reliable. There are no bugs or glitches. It doesn't crash or freeze. 

We have very few users. I'm not sure how the solution would scale. We're more interested in availability, not scalability. 

Technical support is helpful and responsive. I just started a new project with a customer, and they do have support to ensure its original success. After that, it's more important to look to the community to get support. 

I also use Boomi. It's very hard to find human resources for it, however, from a technical aspect, it is pretty similar to Talend. 

The deployment is very easy. It's not too complex. You can use it even if you don't understand the patterns of flows. 

The implementation process takes a few days with some support from the partner vendor, which would be us. Then, a user would have to conceive of the flow, which is more of an internal problem. We have to be clear on the mapping, and that part can be complex. 

The deployment between on-premises and cloud are also a bit different. 

We are able to help clients implement the solution. 

The pricing is confusing. I'm not sure why they have three different products that are expensive, and they don't have anything in the middle. They need something mid-sized. 

The licensing cost is about 40,000 Euros a year. 

There are also implementation prices and other costs. 

We are users of the product. 

We started on the cloud, and now we use it on-premises.

There might be a good mix with Talend and other solutions. You can concentrate all of the complexity in the middleware, and you need to be careful with how you design your flow. It needs to be done properly to avoid issues in the future. 

I'd rate the solution eight out of ten. 

Talend has different modules. Talend has Talend Data integration (DI), Talend Data Quality (DQ), Talend MDM, and Talend Data Mapper (TDM). We have Talend DI, Talend DQ, and TDM. Our use cases span across these modules. We don't use Talend MDM because we have a different solution for MDM. Our EDF team is using an Informatica solution for that.

We have a platform that deals with MongoDB, Oracle, and SQL Server databases. We also have Teradata and Kafka. The first use case was to ensure that when the data traverses from one application to another, there is no data loss. This use case was more around data reconciliation, and it was also loosely tied to the data quality.

The second use case was related to data consistency. We wanted to make sure that the data is consistent across various applications. For example, we are a healthcare company. If I'm just validating the claim system, I need to see how do I inject the data into those systems without any issues. 

The third use case was related to whether the data is matching the configurations. For example, in production, I want to see:

• If there is any data issue or duplicate data?
• Is the data coming from different states getting fed into the system and matching the configurations that have been set in our different engines, such as enrollment, billing, and all those things?
• Is it able to process this data with our configuration?
• Is it giving the right output?

The fourth use case was to see if I can virtually create data. For example, I want to test with some data that is not available in the current environment, or I'm trying to create some EDA files, which are 834 and 837 transaction files. These are the enrollment and claims processing files that come from different providers. If I want to test these files, do I have the right information within my systems, and who can give me that information.

The fifth use case was related to masking the information so that in your environment, people don't have access to certain data. For example, across the industry, people pull the data from production and then just push it into the lower environment and test, but because this is healthcare data, we have a lot of PHI and PII information. If you have your PHI and PII information in production and I am pulling that data, I have everything that is in production in the test environment. So, I know your address, and I know your residents. I can hack into your systems, and I can do anything. This is the main issue for us with HIPAA compliance. How do we mask that information so that in your environment, people don't have access to it?

These are different use cases on which we started our journey. Now, it is going more into the cloud, and we are using Talend to interact with various cloud environments in AWS. We are also interacting with Redshift and Snowflake by using Talend. So, it is expanding. We are using version 7.1, and we are migrating to version 7.3 very soon.

It is saving a lot of time. A person doesn't need to sit and create a file to test. Instead, there are automation processes that are like self-service, and with a few clicks, people are able to generate the data and process it to complete the testing. This gives more confidence in the quality of the deployment that happens in production. The outages have also reduced.

Overall, from 2017 to 2020, we have almost saved around 140,000 to 160,000 hours, which is only with respect to the data. I don't know how much we have saved because of masking. If masking is not there and compliance-related things come up, it could be $2 billion to $3 billion of expense that a company has to bear. Because masking is there, it gives more confidence. Not having the PHI and PII footprint in the lower environment has helped our organization.

It is saving a lot of time. Today, we can mask around a hundred million records in 10 minutes. Masking is one of the key pieces that is used heavily by the business and IT folks. Normally in the software development life cycle, before you project anything into the production environment, you have to test it in the test environment to make sure that when the data goes into production, it works, but these are all production files. For example, we acquired a new company or a new state for which we're going to do the entire back office, which is related to claims processing, payments, and member enrollment every year. If you get the production data and process it again, it becomes a compliance issue. Therefore, for any migrations that are happening, we have developed a new capability called pattern masking. This feature looks at those files, masks that information, and processes it through the system. With this, there is no PHI and PII element, and there is data integrity across different systems. 

It has seamless integration with different databases. It has components using which you can easily integrate with different databases on the cloud or on-premise. 

It is a drag and drop kind of tool. Instead of writing a lot of Java code or SQL queries, you can just drag and drop things. It is all very pictorial. It easily tells you where the job is failing. So, you can just go quickly and figure out why it is happening and then fix it.

They don't have any AI capabilities. Talend DQ is specifically for data quality, which only has data profiling. With Talend DQ, I cannot generate any reports today, so I need an ETL tool. It provides general Excel files, or I have to create some views. If instead of buying a new tool, Talend provides a reporting capability or solution, it would be great. It will reduce the development effort for creating these kinds of reports.

We also manage the infrastructure for Talend. From the licensing perspective, for cloud, they only have seat licenses where one person is tied to one license, but for on-premise, they have concurrent licenses. It would be really awesome if they can provide concurrent licenses for the cloud so that if one person is not there, somebody else can use that license. Currently, it is not possible unless a person deactivates his or her license and moves the same seat license to someone else. We are one of the biggest customers in the central zone of the US for Talend, and this is the feedback that we have provided them again and again, but they come back and say that they aren't able to provide concurrent licenses on the cloud.

In version 7.3, there is a feature for tokenization and de-tokenization of data. This is the feature that we are looking for. It is useful if somebody wants to see what we have masked and how do we demask it. This feature is not there in version 7.1. There are also a few other capabilities on the cloud, but we don't yet have a big footprint in the cloud.

We have been using this solution since 2017. I was the person who brought this solution into this organization.

It is stable. I haven't seen any kind of outages for Talend DQ.

Scalability depends on how many job servers you have. For example, if you have one job server and you are trying to process 2 million, 3 million, or 1 billion records, it might take more time. If you have more job servers so that you can run these jobs in parallel, your jobs will run faster. Networking also comes into play. For example, I am in California, and if I am trying to access something in North Carolina and process data, it could be slow. If my server is located in California, it would be pretty fast.

In terms of the number of users, DQ is specific to the data governance team that has five to seven people. For the Talend solution as a whole, we have around 150 people. It is a big solution, but its maintenance is not that big effort because you are not writing any code. If you know Talend and a little bit of Java, managing it should not be a that big effort.

Sometimes, we have challenges because they don't understand the business, but we have to explain it to them. I can't expect them to understand everything about healthcare and then give me a solution. They provide services to a lot of different industries. 

They have been pretty responsive. We are in the high-tier, and they have defined SLAs in terms of the turnaround time for any kind of issues. They have definitely been very helpful. In the past, when we were not in that particular tier, we had some challenges where it took a little bit of time in getting a response. Sometimes, they also sent some weird responses, and we had to go back and forth, but for a showstopper, their response has been pretty good.

We were able to save 140,000 to 160,000 hours based on the solutions and capabilities that we have built from 2017 to 2020. If I multiply it by $80, it would be somewhere around a billion dollars that we have already saved. If I take five licenses for three years, the savings would be $350,000. I don't know the ROI with respect to Informatica. Slowly, our EDF team is switching over from Informatica to Talend, and they say that it is pretty huge.

It is cheaper than Informatica. Talend Data Quality costs somewhere between $10,000 to $12,000 per year for a seat license. It would cost around $20,000 per year for a concurrent license. It is the same for the whole big data solution, which comes with Talend DI, Talend DQ, and TDM.

I would rate Talend Data Quality 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.