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Azure Data Factory vs TIBCO Scribe comparison

 

Comparison Buyer's Guide

Executive Summary

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
3rd
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
93
Ranking in other categories
Cloud Data Warehouse (2nd)
TIBCO Scribe
Ranking in Data Integration
49th
Average Rating
6.0
Reviews Sentiment
6.2
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Data Integration category, the mindshare of Azure Data Factory is 3.2%, down from 10.0% compared to the previous year. The mindshare of TIBCO Scribe is 0.5%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory3.2%
TIBCO Scribe0.5%
Other96.3%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
Integrates diverse data sources and streamlines ETL processes effectively
Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.
GouravSuri - PeerSpot reviewer
Software Engineer (L4) at Uber
A cloud solution that has a lot of connectors, but it should provide better documentation and scenario-based samples
Smaller customers who want to integrate with other systems don't need too much in-house expertise in terms of technology. They can hire consultants who can implement this solution for them, and they don't have to maintain any infrastructure. For a smaller setup, it is a go-to integration system wherein they don't need a lot of expertise or infrastructure. The solution's UI is pretty intuitive and easy. It is good for smaller integration use cases. I think it would be a problem for bigger use cases. Overall, I rate TIBCO Scribe a six out of ten.

Quotes from Members

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

Pros

"The most valuable aspect is the copy capability."
"From what we have seen so far, the solution seems very stable."
"Azure Data Factory is a low code, no code platform, which is helpful."
"The initial setup is very quick and easy."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"It's extremely consistent."
"The solution has a good interface and the integration with GitHub is very useful."
"Azure Data Factory is a low code, no code platform, which is helpful."
"The most valuable feature of TIBCO Scribe is the connectors available to various products."
 

Cons

"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"Data Factory's performance during heavy data processing isn't great."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"Some of the optimization techniques are not scalable."
"The one element of the solution that we have used and could be improved is the user interface."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"The Microsoft documentation is too complicated."
"The solution should provide better documentation and scenario-based samples."
 

Pricing and Cost Advice

"I don't see a cost; it appears to be included in general support."
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"The pricing model is based on usage and is not cheap."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"The licensing cost is included in the Synapse."
"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
"Understanding the pricing model for Data Factory is quite complex."
"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
9%
Government
7%
Computer Software Company
19%
Manufacturing Company
11%
Financial Services Firm
9%
Insurance Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise57
No data available
 

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...
What do you like most about TIBCO Scribe?
The most valuable feature of TIBCO Scribe is the connectors available to various products.
What needs improvement with TIBCO Scribe?
The solution should provide better documentation and scenario-based samples.
What is your primary use case for TIBCO Scribe?
TIBCO Scribe is used to do some out-of-the-box integration with some packaged products. TIBCO Scribe is a cloud solution that has a lot of connectors, like REST connectors and database connectors t...
 

Also Known As

No data available
Scribe
 

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
Armanino, Oklahoma City Thunder, Texas Rangers, Tata Technologies, BenefAction, Indianapolis Motor Speedway, Atdec, Dynasplint Systems
Find out what your peers are saying about Microsoft, Informatica, IBM and others in Data Integration. Updated: January 2026.
881,082 professionals have used our research since 2012.