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Azure Data Factory vs Palantir Foundry comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024

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)
Palantir Foundry
Ranking in Data Integration
12th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
17
Ranking in other categories
IT Operations Analytics (10th), Supply Chain Analytics (1st), Cloud Data Integration (11th), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)
 

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 Palantir Foundry is 2.3%, down from 2.5% 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%
Palantir Foundry2.3%
Other94.5%
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.
SR
Architect at L&T Technology Services
Finds security and customization features impressive, although cost concerns persist
My experience with Palantir Foundry and Azure has been good. Palantir Foundry is costly, but Azure is open, which allows for easier experimentation. Being a closed product, Palantir Foundry is difficult to practice offline unless we have an enterprise edition. However, it is very secure compared to other platforms. Palantir Foundry's best features include security, built-in features, low-code, no-code platform, and ease of use. The collaborative workspaces within Palantir Foundry contribute to team efficiency and project outcomes through seamless operation. The ease of customization is particularly notable. I have worked with the data lineage feature in Palantir Foundry, which comes by default. We simply need to tick the checkbox and make necessary configuration changes within the system itself. We do not need to procure another lineage platform as Palantir Foundry has its own built-in features for data lineage, data governance, and data security. The lineage feature helps enhance our data management practices by allowing us to understand the origin of data, track all activities happening on the data, identify users and consumers, and monitor how it flows across the system. This makes it easier to generate reports based on the lineage database. The predictive analytics capability within Palantir Foundry impacts financial forecasting strategies through its AIP functionality, which includes numerous pre-built models, LLMs, and data science application libraries. Using the AIP library within Palantir Foundry helps us develop quick resolutions for predictive models and analytics.

Quotes from Members

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

Pros

"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The most important feature is that it can help you do the multi-threading concepts."
"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
"I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"The most valuable aspect is the copy capability."
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"Great features available in one tool."
"The solution offers very good end-to-end capabilities."
"The virtualization tool is useful."
"The data lineage is great."
"The security is also excellent. It's highly granular, so the admins have a high degree of control, and there are many levels of security. That worked well. You won't have an EDC unless you put everything onto the platform because it is its own isolated thing."
"Encapsulates all the components without the requirement to integrate or check compatibility."
"The interface is really user-friendly."
 

Cons

"The pricing model should be more transparent and available online."
"The setup and configuration process could be simplified."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"When working with AWS, we have noticed that the difference between ADF and AWS is that AWS is more customer-focused. They're more responsive compared to any other company. ADF is not as good as AWS, but it should be. If AWS is ten out of ten, ADF is around eight out of ten. I think AWS is easier to understand from the GUI perspective compared to ADF."
"The product could provide more ways to import and export data."
"The Microsoft documentation is too complicated."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"If you want to create new models on specific data sets, computing that is quite costly."
"Cost of this solution is quite high."
"Difficult to receive data from external sources."
"The startup pricing is high, causing concern despite being cost-effective in terms of total cost of ownership."
"The workflow could be improved."
 

Pricing and Cost Advice

"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."
"I would not say that this product is overly expensive."
"The solution's pricing is competitive."
"The price is fair."
"The price you pay is determined by how much you use it."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."
"Palantir Foundry has different pricing models that can be negotiated."
"It's expensive."
"Palantir Foundry is an expensive solution."
"The solution’s pricing is high."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
9%
Government
7%
Manufacturing Company
14%
Financial Services Firm
10%
Government
8%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise57
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise5
Large Enterprise8
 

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 needs improvement with Palantir Foundry?
Apart from the pricing and offline availability issues, improvements are needed in Palantir Foundry's costing factor. Cost-wise, it is not open for everybody, and they are not exposing anything out...
What is your primary use case for Palantir Foundry?
One of the leading European manufacturing plants uses Palantir Foundry for manufacturing interior parts of various car brands such as Honda, Hyundai, Ford, Mercedes-Benz, and BMW. This involves hig...
What advice do you have for others considering Palantir Foundry?
Palantir Foundry is an excellent product for data engineering. On a scale of one to 10, I would rate Palantir Foundry a 9.
 

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
Merck KGaA, Airbus, Ferrari,United States Intelligence Community, United States Department of Defense
Find out what your peers are saying about Azure Data Factory vs. Palantir Foundry and other solutions. Updated: December 2025.
881,082 professionals have used our research since 2012.