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Azure Data Factory vs Talend Open Studio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 20, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (2nd)
Talend Open Studio
Ranking in Data Integration
5th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
50
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Data Integration category, the mindshare of Azure Data Factory is 7.9%, down from 12.2% compared to the previous year. The mindshare of Talend Open Studio is 4.4%, down from 5.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

Featured Reviews

Joy Maitra - PeerSpot reviewer
Facilitates seamless data pipeline creation with good analytics and and thorough monitoring
Azure Data Factory is a low code, no code platform, which is helpful. It provides many prebuilt functionalities that assist in building data pipelines. Also, it facilitates easy transformation with all required functionalities for analytics. Furthermore, it connects to different sources out-of-the-box, making integration much easier. The monitoring is very thorough, though a more readable version would be appreciable.
Costin Marzea - PeerSpot reviewer
Allows you to develop your own components and can be used as an OEM
Sometimes, scalability is part of planning. It depends on what you mean by scalability. People talk a lot about it, but scalability is not always about system functionality. Sometimes, it may be planning the job you're doing. If you want to split it into several jobs or servers, you don't actually have to have it built in as a functionality. You can create a job using a loop, which runs and controls several jobs in a loop that may be controlled. Scaling should not always be part of the infrastructure based on whether the engine can scale or not. I think it's your plan or project that should scale and split, and you can define these parameters. These parameters include how many servers you want to run or how many executions you want to do on different parts of the data. It's not always an issue of the engine running. Sometimes, your database should be configured to support partitioning. The product may scale very well without partitioning, but if the basic response is very slow, you didn't solve the problem. You should solve the problems at a higher level, not just at the execution level. They should be solved at the database level and communication level, and you should have firewalls. We are trying to add to the open source the ability to generate code for containers and Kubernetes that exist in the subscription version. Once you do this, Kubernetes will take care of the scaling, so there is no problem.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"Powerful but easy-to-use and intuitive."
"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."
"Data Factory's best features are simplicity and flexibility."
"It is easy to integrate."
"I like its integration with SQL pools, its ability to work with Databricks, its pipelines, and the serverless architecture are the most effective features."
"We have been using drivers to connect to various data sets and consume data."
"The main differentiator that I have seen between Talend and other data integration tools is the ability to view the data pipeline in the form of a program."
"There are many architectures: hybrid, cloud, and on-prem."
"The API integration and big data approach are very good because of how you extract data from JSP files or big data web repositories like MongoDB."
"The standout feature for me is the user-friendly nature of the components."
"Talend Open Studio's installation process is easy. One just needs to install Java before installing the product"
"Open Studio's best features are that it's user-friendly, even for beginners, and very easy to implement."
"The solution's technical support is responsive and helpful."
"Talend can connect to multiple data sources, including relational data sources, ERP, CRM, and others."
 

Cons

"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"Azure Data Factory can improve by having support in the drivers for change data capture."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"Data Factory's cost is too high."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"Data Factory could be improved in terms of data transformations by adding more metadata extractions."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"There is a need for mastery in some areas."
"It is not as visually appealing as some of the other tools."
"The stability of the solution could improve when running jobs. There can be errors when running projects but in the end, it works well and the errors do not impact the result."
"We don't get continuous replication of the data."
"Multiple products are there within the product suite. That can be actually trimmed down."
"The technical support and documentation need a lot of work to come up to standard."
"I would say that writing to JSON is kind of a pain. It reads from a JSON file pretty well, but writing to a JSON file is not so great because its components are not good."
"The security features could be improved."
 

Pricing and Cost Advice

"The solution's pricing is competitive."
"The licensing cost is included in the Synapse."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"It's not particularly expensive."
"The pricing model is based on usage and is not cheap."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"Pricing appears to be reasonable in my opinion."
"I don't see a cost; it appears to be included in general support."
"We are using the free version of the tool, because the enterprise version is a little expensive."
"Talend Open Studio costs about 11,000 a year."
"It is an open-source product."
"For Talend Open Studio, there is a need to make yearly payments towards the licensing cost. Talend Open Studio is a bit expensive, in my opinion."
"I am using the open-source version of the solution, so there are no extra costs for any feature."
"Pricing is always a challenge. It is quite an expensive model, but because the platform is so simple to use, we haven't had to purchase any additional licenses."
"The solution will be more expensive if you have a low data volume and a large number of developers."
"Open Studio has a basic license and additional costs for services, including customer support and technical assistance."
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Top Industries

By visitors reading reviews
Financial Services Firm
14%
Computer Software Company
12%
Manufacturing Company
9%
Government
6%
Financial Services Firm
17%
Computer Software Company
13%
Manufacturing Company
8%
Government
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Azure Data Factory compare with Informatica PowerCenter?
Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...
How does Azure Data Factory compare with Informatica Cloud Data Integration?
Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...
How does Talend Open Studio compare with AWS Glue?
We reviewed AWS Glue before choosing Talend Open Studio. AWS Glue is the managed ETL (extract, transform, and load) from Amazon Web Services. AWS Glue enables AWS users to create and manage jobs in...
What do you like most about Talend Open Studio?
It is easy to use and covers most of the functions needed. We can use the code without any extra effort. The open source is very good. They have the same commercials with additional connectors. The...
 

Also Known As

No data available
Open Studio
 

Overview

 

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
Almerys, BF&M, Findus
Find out what your peers are saying about Azure Data Factory vs. Talend Open Studio and other solutions. Updated: July 2025.
862,452 professionals have used our research since 2012.