Qlik Talend Cloud Reviews

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. 

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.

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 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.

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.