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Practice Head, Analytics at a tech services company with 201-500 employees
Real User
Top 5
Useful data stewardship and data preparation features but lacks documentation
Pros and Cons
  • "The data integration aspect of the solution is excellent."
  • "The documentation is lacking within the product. They need to get better at all aspects of describing how it works and how to use it."

What is our primary use case?

One use case for Talend Data Management is using it for data integration from multiple data sources and then to build our customer's golden record.

What is most valuable?

The data integration aspect of the solution is excellent.

The product's data preparation features are very good.

There's very useful data stewardship within the product.

From a technical standpoint, the solution itself is pretty good.

There are very good pre-built connectors in Talend, which is good for many clients or businesses, as, in most cases, companies are dealing with multiple data sources from multiple technologies. That is where a tool like Talend is extremely helpful. 

What needs improvement?

While I can't really speak to technical aspects that need improvement, I can say that the marketing of the solution is not the best. They need to really improve how they present the solution to potential clients.

The documentation is lacking within the product. They need to get better at all aspects of describing how it works and how to use it.

For example, if you see all the other modern products - like Snowflake or Cloudera - and go to their sites, you will have various types of documentation, including data charts and other things you can get readily access. However, if you go to the Talend site, you won't get this kind of marketing material, which is always required.

It's especially difficult when you're writing a proposal. You need to have the technical specifications. All of these modern solutions are quite advanced in terms of their documentation. You go to their site, and you will get a lot of brochures, reports, and other items. This gives you a detailed understanding of the solutions, and all of the specifications, and technicalities, and other things. For Talend, unfortunately, there's a lag in that aspect. 

From a data management or data engineering perspective, they could stabilize their new features more. 

It would be ideal if they could consider adding prescriptive or predictive models into their components, such as an alternate data IQ.

For how long have I used the solution?

We don't use the solution directly per se. We're a solutions provider.

What do I think about the stability of the solution?

The solution offers very good stability. We haven't really had any issues with glitches or bugs. There aren't problems with the solution crashing or freezing on clients. It's been good.

What do I think about the scalability of the solution?

The solution is good at scalability With their big data integration products, they're actually handling a huge data set and the scalability of it is good. They're well-positioned to scale for the client.

How are customer service and technical support?

Technical support is pretty good. I would rate them at an eight out of ten. We've been satisfied with the level of support provided overall.

How was the initial setup?

The initial setup was straightforward. It was not complex at all.

Deployment times vary according to the size of the client or their needs. If it's for a big client and big project, it could be six months of implementation time. Smaller jobs may only take one month of implementation. It all depends.

What about the implementation team?

We tend to implement this solution for our clients.

Which other solutions did I evaluate?

I've briefly looked at Azure Data Factory and have been seeking out comparisons between these two options.

What other advice do I have?

We are the solution provider. We are the company that implements such solutions as Talend. We are not the user of the solutions themselves necessarily. We are the reseller as well as implementation partners and the implementing partner of Talend. We have resources who work on these tools and who implement solutions for our customers. 

The version of the solution we use varies from client to client, especially for Talend. They have multiple products, including Talend Data Management and Talend's Data Integration Pipeline. A lot of our customers are using the open-source version of Talend. Some are using the licensed version. From those, some are using it for the data stewardship. 

We deploy both cloud and on-premises models as required.

Overall, we've been happy with the product. From a data management perspective, the data preparation, data stewardship and the other features that Talend has, including the MDM, are all extremely important for today's organization.

Overall, I would rate the solution at a seven out of ten.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Data Analytics Specialist at a pharma/biotech company with 10,001+ employees
Real User
Top 5Leaderboard
Good performance and integration, but needs more training documentation
Pros and Cons
  • "The most valuable feature is the performance. Denodo is very useful, especially in this huge pharma environment. I've found that older SAP solutions were very tightly coupled to each other, which resulted in data restrictions. Getting data from different sources was tough and tedious. Compared to these old solutions, Denodo is very easy to work with for the analytical team. Now that we've implemented this virtualization layer, we are capable of getting the data very smoothly. We implemented a very small unit, but the performance and integration have been very good."
  • "Denodo's training documentation could be improved by providing more material. From an administrative standpoint, I've found that only Denodo websites provide the usual tutorials. It may be because it's a bit of a restricted tool, but it results in trouble with learning. Normally, I can find help and solutions from other sources, but I haven't been able to find any for Denodo. Other that, it's fine and it performs well. I only have six months of experience, so I can't accurately suggest improvements."

What is our primary use case?

My primary use case is for data virtualization. I'm working in the pharma domain, so there are large amounts of data coming in from different sources, which I aggregate into Azure SQL, some other web services, and SAP applications like CRM, POS, and others. Denodo acts as a virtualization layer, where we are collecting and creating views for analytical purposes. So we use Denodo to integrate and transform. It is deployed on-premises. 

What is most valuable?

The most valuable feature is the performance. Denodo is very useful, especially in this huge pharma environment. I've found that older SAP solutions were very tightly coupled to each other, which resulted in data restrictions. Getting data from different sources was tough and tedious. Compared to these old solutions, Denodo is very easy to work with for the analytical team. Now that we've implemented this virtualization layer, we are capable of getting the data very smoothly. We implemented a very small unit, but the performance and integration have been very good. 

What needs improvement?

Denodo's training documentation could be improved by providing more material. From an administrative standpoint, I've found that only Denodo websites provide the usual tutorials. It may be because it's a bit of a restricted tool, but it results in trouble with learning. Normally, I can find help and solutions from other sources, but I haven't been able to find any for Denodo. Other that, it's fine and it performs well. I only have six months of experience, so I can't accurately suggest improvements. 

For how long have I used the solution?

I have been using Denodo for almost six months. 

What do I think about the stability of the solution?

Currently, we haven't found any discrepancies or problems with stability. We are a team of 25 using Denodo simultaneously, but we're still in a development and testing environment. Once it's in production, we'll be able to tell if there's any kind of a bottleneck. 

What do I think about the scalability of the solution?

Our Denodo administrative team created a pipeline from one environment to another environment and we haven't had any trouble yet. The production deployment is going to happen soon, so within a month or so we'll have a better understanding of the production data, streaming data, and all that. 

How are customer service and support?

Technical support is fine. I will say that my team and I are interested in pursuing a Denodo certification. I was looking for a way to take the Denodo Administrative Certification 8.0, but I'm still waiting on that. Developers are restricted right now because we need to pay for each and every topic in Denodo. 

Which solution did I use previously and why did I switch?

I have worked with Azure Data Factory. With Data Factory, you need a staging solution, a physical data platform for storage. Denodo doesn't need this, since it has a cache mechanism. 

I have also previously worked with TIBCO, but that was more service integration than data integration. 

How was the initial setup?

The initial setup was easy. Also, connecting Denodo to Power BI was a smooth process. It was simple to connect it with a Denodo ODBC connector. So installation and connecting with other tools were both very easy. 

For deployment and maintenance, we are a team of two developers. It took around eight months to develop the unified views. 

What about the implementation team?

I implemented through an in-house team. 

What other advice do I have?

I rate this solution a seven out of ten. I work in an advanced analytical area where it helps to reduce bottlenecks, extract from different application sources, improves performance, and gets the desired data, which is what those endpoint people need. 

If you have a vast data fabric or data mesh architectural framework, I definitely suggest implementing this kind of a virtualization layer. I recommend Denodo because it has been very easy, compared to other integration platforms. 

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Director at a financial services firm with 10,001+ employees
Real User
Very easy for a person to get acquainted with and very useful for working with different sources
Pros and Cons
  • "The greatest feature is that it is very easy to have someone come in and jump right in. It is one of the nicest tools in terms of getting a person acquainted quickly."
  • "What I didn't like about it is that the platform itself is not great at distributed processing. When you need high parallel processing, it has some inherent issues. We had to use Java transformation, and it did not go very well. I have heard that it is going to the cloud, but we haven't tried that."

What is our primary use case?

We are generally using it for ETL systems. 

How has it helped my organization?

We have different sources of files coming, sometimes from the mainframe, and Informatica is very useful for accepting different sources, such as spreadsheets and databases, and doing the transformation. Libraries are also helpful. I also managed to go into the database and create my own transformations for mapping automatically.

What is most valuable?

The greatest feature is that it is very easy to have someone come in and jump right in. It is one of the nicest tools in terms of getting a person acquainted quickly.

What needs improvement?

What I didn't like about it is that the platform itself is not great at distributed processing. When you need high parallel processing, it has some inherent issues. We had to use Java transformation, and it did not go very well. I have heard that it is going to the cloud, but we haven't tried that. We have Informatica in-house, and we thought it would go with ADF. Microsoft is leaning on us to do that because we're working with Azure. So, I don't know much about cloud features.

The pricing model is a little bit difficult for development tools versus running the system in production. So, it has some shortcomings, but in general, it is not bad.

For how long have I used the solution?

We worked with PowerCenter for a long time, and we have a site license for it.

What do I think about the stability of the solution?

We haven't had major problems. The issues that we had were more around libraries linking to other places. It was not so flexible there. For example, we used a Java transformation that needed a separate set of libraries, but they collided if a library was used by Informatica. So, we had to stay a version earlier. Although it seemed a little bit rigid on what it can provide in terms of the ability to go into the database and create your own stuff, I wrote a makefile that actually went into the database, and based on the parameters that people put in the makefile, created transformation for them.

What do I think about the scalability of the solution?

I wasn't impressed with its ability to process in parallel. Some of it is inherited in the tool, and there is nothing you can do about it. For example, if you have aggregation, then everything stops, which is okay and I understand that, but for the other things also, I didn't see it scalable. I have heard that it is better in new releases.

Which solution did I use previously and why did I switch?

I work with Azure Data Factory and Informatica PowerCenter, and we are now considering taking either of them. We did work with PowerCenter for a long time, and we have a site license for it. With ADF, we're just starting.

What's my experience with pricing, setup cost, and licensing?

We have a site license, but we do pay by the division.

What other advice do I have?

I would rate it an eight out of 10.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Director at a university with 1,001-5,000 employees
Real User
Top 10
Good reporting, but resource utilization is high and technical support can be improved
Pros and Cons
  • "The most valuable features are data virtualization and reporting."
  • "The technical support could be a little better."

What is our primary use case?

We are a consultancy company that specializes in data integration and we advise clients on what solutions will best meet their requirements.

What is most valuable?

The most valuable features are data virtualization and reporting. Data virtualization has to do with integrating data from different databases. 

What needs improvement?

The utilization of system resources is high.

The technical support could be a little better.

Having a "lite" version for a reduced price would be of interest to smaller companies.

For how long have I used the solution?

I only began using the IBM Cloud Pak for Data recently, although some of the people in my team have been using it for close to a year and a half.

What do I think about the stability of the solution?

This product is stable enough. Like with all of these large packages, it's a combination of different tools that have been brought together, so stability is always an issue with them. It will improve over time.

What do I think about the scalability of the solution?

IBM has enough experience with scalability, although I have found that the Cloud Pak for Data uses a lot of resources. It is not very resource-economic.

How are customer service and technical support?

We get support directly from IBM and I would say that it's good enough. It can be better, but at this point, it's sufficient.

Which solution did I use previously and why did I switch?

These integration-type products have a lot of things that are now pre-packaged in a cloud environment. There are similar solutions in Microsoft Azure, Google, and others. From an educational or teaching perspective, I think that as these systems start to have more influence in the market, there will be big a bigger concentration on them in terms of training.

At this point, I have only looked at them in a general sense and have not had much hands-on experience. I have focused more on open-source products.

What was our ROI?

I think that this solution is targetted for larger companies and it would be of interest to them, but for smaller companies, it would be tough to get value out of it.

What's my experience with pricing, setup cost, and licensing?

I think that this product is too expensive for smaller companies.

What other advice do I have?

I would rate this solution a five out of ten.

Disclosure: My company has a business relationship with this vendor other than being a customer: partner
Get our free report covering Informatica, Informatica, Talend, and other competitors of Azure Data Factory. Updated: January 2022.
563,208 professionals have used our research since 2012.