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Azure Data Factory vs Palantir Foundry comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 19, 2024
 

Categories and Ranking

Azure Data Factory
Ranking in Data Integration
1st
Average Rating
8.0
Reviews Sentiment
6.9
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
7.1
Number of Reviews
15
Ranking in other categories
IT Operations Analytics (3rd), 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 December 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.0%, down from 13.3% compared to the previous year. The mindshare of Palantir Foundry is 2.6%, down from 3.0% 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 scalability of the product is impressive."
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"The best part of this product is the extraction, transformation, and load."
"I am one hundred percent happy with the stability."
"It is easy to integrate."
"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."
"We have been using drivers to connect to various data sets and consume data."
"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."
"The virtualization tool is useful."
"The interface is really user-friendly."
"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"Encapsulates all the components without the requirement to integrate or check compatibility."
"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."
"I like the data onboarding to Palantir Foundry and ETL creation."
 

Cons

"There's space for improvement in the development process of the data pipelines."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"I have not found any real shortcomings within the product."
"Data Factory's monitorability could be better."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"Some error messages can be very cryptic."
"The frontend capabilities of Palantir Foundry could be improved."
"The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"If you want to create new models on specific data sets, computing that is quite costly."
"Cost of this solution is quite high."
"The workflow could be improved."
"The solution's visualization and analysis could be improved."
 

Pricing and Cost Advice

"Pricing is comparable, it's somewhere in the middle."
"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."
"ADF is cheaper compared to AWS."
"Data Factory is affordable."
"Our licensing fees are approximately 15,000 ($150 USD) per month."
"I would not say that this product is overly expensive."
"The solution is cheap."
"I would rate Data Factory's pricing nine out of ten."
"Palantir Foundry has different pricing models that can be negotiated."
"It's expensive."
"The solution’s pricing is high."
"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%
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: December 2024.
824,053 professionals have used our research since 2012.