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Azure Data Factory vs Palantir Foundry comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
86
Ranking in other categories
Cloud Data Warehouse (3rd)
Palantir Foundry
Ranking in Data Integration
12th
Average Rating
7.6
Reviews Sentiment
7.2
Number of Reviews
15
Ranking in other categories
IT Operations Analytics (4th), Supply Chain Analytics (1st), Cloud Data Integration (10th), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)
 

Mindshare comparison

As of November 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.1%, down from 13.3% compared to the previous year. The mindshare of Palantir Foundry is 2.5%, down from 3.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Integration
 

Featured Reviews

Thulani David Mngadi - PeerSpot reviewer
Data flow feature is valuable for data transformation tasks
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.
Manilal Kasera - PeerSpot reviewer
Transparent with good reliability and good data visibility
The initial setup had a medium level of difficulty. If we go through the documentation, we can learn about what to do. In Palantir, they had a section called Academy, and that Academy was quite useful. If you go through that as a new user, it makes the process easier as you learn what to do. Initially, we didn't have many sources that would help us learn things, so we struggled a bit. In contrast, with Azure and Amazon Cloud, you have many sources from where you would be easily able to learn. You could just Google what you needed with them, as there's so much available documentation online. What was easy was the fact that everything was in one place. With AWS Cloud, there are many applications to support. You can use Glue or Athena, and you have all these other applications. However, with Palantir, everything is easy due to the fact that it is centralized. It's drag and drop and everything is very transparent.

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 feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"The most important feature is that it can help you do the multi-threading concepts."
"We use the solution to move data from on-premises to the cloud."
"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"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."
"Powerful but easy-to-use and intuitive."
"The AI engine that comes with Palantir Foundry is quite interesting."
"Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"Encapsulates all the components without the requirement to integrate or check compatibility."
"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."
"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."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"The virtualization tool is useful."
 

Cons

"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."
"Some of the optimization techniques are not scalable."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"When the record fails, it's tough to identify and log."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"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."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"If you want to create new models on specific data sets, computing that is quite costly."
"The solution's visualization and analysis could be improved."
"Cost of this solution is quite high."
"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."
"The solution’s data security could be improved."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"The frontend capabilities of Palantir Foundry could be improved."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
 

Pricing and Cost Advice

"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."
"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."
"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."
"The cost is based on the amount of data sets that we are ingesting."
"The price you pay is determined by how much you use it."
"This is a cost-effective solution."
"The solution is cheap."
"Azure Data Factory gives better value for the price than other solutions such as Informatica."
"The solution’s pricing is high."
"Palantir Foundry is an expensive solution."
"It's expensive."
"Palantir Foundry has different pricing models that can be negotiated."
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
7%
Manufacturing Company
14%
Financial Services Firm
11%
Computer Software Company
10%
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...
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?
The solution’s data security could be improved. We cannot use many Python packages with the solution. We were able to use only a few compatible Python packages.
What is your primary use case for Palantir Foundry?
Our use cases are mostly related to data analytics. We are building some dashboards and ETL pipelines on the Palantir side. Palantir Foundry is a low-code/no-code platform with a user-friendly UI. ...
 

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: October 2024.
816,406 professionals have used our research since 2012.