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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
1st
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
Cloud Data Warehouse (2nd)
Palantir Foundry
Ranking in Data Integration
14th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
17
Ranking in other categories
IT Operations Analytics (9th), Supply Chain Analytics (1st), Cloud Data Integration (12th), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)
 

Mindshare comparison

As of October 2025, in the Data Integration category, the mindshare of Azure Data Factory is 5.2%, down from 11.0% compared to the previous year. The mindshare of Palantir Foundry is 3.1%, up from 2.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration Market Share Distribution
ProductMarket Share (%)
Azure Data Factory5.2%
Palantir Foundry3.1%
Other91.7%
Data Integration
 

Featured Reviews

KandaswamyMuthukrishnan - PeerSpot reviewer
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.
Rama Subba Reddy Thavva - PeerSpot reviewer
A low-code/no-code platform with a user-friendly UI
We couldn't implement or use some of the latest functionalities, like Spark. Palantir Foundry is scalable, but it is costly compared to other cloud providers. The solution is more suitable for small and medium businesses. It might be difficult for large enterprises. I rate the solution’s scalability a seven 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 best part of this product is the extraction, transformation, and load."
"The data is more scalable."
"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"It is a complete ETL Solution."
"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"We have been using drivers to connect to various data sets and consume data."
"The interface is really user-friendly."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"The virtualization tool is useful."
"It's scalable."
"I like the data onboarding to Palantir Foundry and ETL creation."
"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"I rate Palantir Foundry a ten out of ten."
"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."
 

Cons

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."
"Data Factory's performance during heavy data processing isn't great."
"There is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration."
"When the record fails, it's tough to identify and log."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."
"There are limitations when processing more than one GD file."
"The solution could use more online documentation for new users."
"The workflow could be improved."
"The major hindrance with Palantir Foundry is that being a very closed product, the cost optimization and costing are not exposed to the end users."
"Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure."
"Difficult to receive data from external sources."
"Cost of this solution is quite high."
 

Pricing and Cost Advice

"ADF is cheaper compared to AWS."
"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."
"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."
"It's not particularly expensive."
"Data Factory is expensive."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The cost is based on the amount of data sets that we are ingesting."
"The solution is cheap."
"The solution’s pricing is high."
"It's expensive."
"Palantir Foundry has different pricing models that can be negotiated."
"Palantir Foundry is an expensive solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
Manufacturing Company
13%
Financial Services Firm
10%
Government
7%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
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 do you like most about Palantir Foundry?
Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration.
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...
 

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: September 2025.
871,408 professionals have used our research since 2012.