Qlik Talend Cloud Reviews

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.

We have multiple use cases for this solution. We integrate with Salesforce, SAP and Oracle databases to build business logic and provide reporting.

The transformations are built in and come as a package for us to drag and drop and complete in one go. The main platform of this tool is Java and if you know how to use Java, it will be a very easy tool to use.

Sometimes there are bugs which are unidentified and we have to follow-up with the Talend team to resolve them. In a critical situation, it takes time for them to update patches. In a month, we get two or three patches and this means we need to redeploy completed jobs. 

I have been using this solution for three years. 

This is a stable solution. 

This is a scalable solution. 

The technical support team are very supportive and respond quickly based on the severity of the ticket we raise.

Positive

This solution is very easy to set up and deployment is quick. 

Deployment was completed by an in-house team. 

This is a good tool. If your use case depends on heterogeneous sources like Salesforce, SAP, and some other product information systems, I would recommend Talend. 

I would rate it an eight out of ten. 

My main use of this solution is application integration.

I like everything about this product, but the biggest thing is the ease of use. I also like the ability to have files between Linux missions.

An area for improvement would be that securing the product can be challenging because there are multiple ways to secure everything. If they offered a secured version or sample, that would be a big improvement.

I've been using this solution for around ten years. I use it myself and have trained others in how to use it.

I've had some issues with bugs causing crashes, especially when making changes to the system or with the monthly upgrades to Studio they've introduced. However, the stability is great when you scale up to more users or more data.

Currently, scaling is more effective with the cloud version, where you can get into the data fabric and have access to more power.

I've had no problems with tech support - they're willing to work with me as an advanced user to figure out solutions.

Talend is scaled much lower in cost than Informatica.

Talend is getting ready to drop SVN as a code repository in version 8 going forward, so you have to convert anybody who's using SVN to switch over to GIT (on which it runs much faster). I understand it isn't easy to get people who are experienced in this product, so you should go through the training on their website as there are a lot of nuances in this product that you may miss otherwise. I would rate this solution as ten 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 use Talend to help our clients distribute their products.

It's a real ETL solution. You download data, transform it, and then you upload it.

The most valuable feature is integration. There are several connectors, there is a large community, and the solution updates often. It is always up-to-date.

Performance and speed could be improved.

They could improve on the scalability.

I have been using the Talend Data Management Platform for three or four years.

This solution is stable, although not the best. I'd say that it is great but not on the top.

It's scalable but they could use Docker to improve the scalability.

I have contacted technical support on occasion. It's great because you have the support and you have the community as well.

You create a ticket and intel is very open and reactive. They are friendly.

The initial setup is straightforward and not complex.

It did not take a lot of time to deploy.

Before choosing Talend, we evaluated DataStage and Informatica.

We decided to go with Talend because it has an open-mindedness to it. It's not a closed system, you have a great community and in France, where I live, it is the leader and there are many people who can do a good job using Talend.

Talend is a good product and I recommend it. It is an open system, you have several connectors, it's evolved, and you are unlimited because of the great community. There are always people who may have had some of the same encounters and have found solutions to the same problems. 

I am still learning this product myself, I have not yet used all of the features this product has to offer.

I would rate this solution an eight out of ten.

Data Quality easily identifiable instead of manual finding and cleaning the data through Excel (earlier used to follow) before ETL

Currently the best open source data quality tool available as compared to other open DQ tools ('DataCleaner', 'Open Source Data Quality & Profiling') for of a variety of reasons:

1. Vast connectors to different DB, Web, CRM, etc
2. Custom code is allowed
3. Wide range of advanced algorithms
4. Recommended for advanced users
5. Detailed analysis, etc
6. Large community of users

The most valuable features for us are: custom code, connectors, algorithms.

As it is a open source tool, some minor bugs are there.

Fairly straightforward. Lots of user guides and tutorials are available to get started.

The best part is that it is open source.

Great product, surely give it a try.