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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 February 2026, in the Data Integration category, the mindshare of Azure Data Factory is 3.0%, down from 9.8% compared to the previous year. The mindshare of Palantir Foundry is 2.1%, 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 Factory3.0%
Palantir Foundry2.1%
Other94.9%
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

"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."
"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"It is easy to deploy workflows and schedule jobs."
"We haven't had any issues connecting it to other products."
"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."
"Data Factory's best features are simplicity and flexibility."
"Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration."
"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."
"Great features available in one tool."
"The ease of use is my favorite feature. We're able to build different models and projects or combine different projects to build one use case."
"I like the data onboarding to Palantir Foundry and ETL creation."
"The AI engine that comes with Palantir Foundry is quite interesting."
"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."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
 

Cons

"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."
"Some known bugs and issues with Azure Data Factory could be rectified."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."
"Data Factory's cost is too high."
"Lacks in-built streaming data processing."
"The Microsoft documentation is too complicated."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive."
"The startup pricing is high, causing concern despite being cost-effective in terms of total cost of ownership."
"If you want to create new models on specific data sets, computing that is quite costly."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"The workflow could be improved."
"Difficult to receive data from external sources."
"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."
"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."
 

Pricing and Cost Advice

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."
"The pricing model is based on usage and is not cheap."
"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."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
"I don't see a cost; it appears to be included in general support."
"Product is priced at the market standard."
"Data Factory is expensive."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"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
6%
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: February 2026.
881,733 professionals have used our research since 2012.