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Azure Data Factory vs Palantir Gotham comparison

 

Comparison Buyer's Guide

Executive Summary

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 Gotham
Ranking in Data Integration
50th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

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 Gotham is 0.8%, up from 0.4% 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 Gotham0.8%
Other94.0%
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.
WH
A seamless all-in-one solution
This solution is seamless. From one platform, we can do just about anything. With other solutions, you'll need a separate platform for data ingestion, manipulation, etc. Then you'll need another tool for reporting. Palantir Gotham literally does it all. It generates a report regardless of the format. It can seamlessly generate it after the data has been collected.

Quotes from Members

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

Pros

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"The overall performance is quite good."
"Allows more data between on-premises and cloud solutions"
"It is beneficial that the solution is written with Spark as the back end."
"Powerful but easy-to-use and intuitive."
"The data flows were beneficial, allowing us to perform multiple transformations."
"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."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"This solution is seamless. From one platform, we can do just about anything."
 

Cons

"I do not have any notes for improvement."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"Some of the optimization techniques are not scalable."
"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."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."
"Azure Data Factory can improve by having support in the drivers for change data capture."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"I think there should be less coding involved. Currently, using it involves a tremendous amount of coding."
 

Pricing and Cost Advice

"Our licensing fees are approximately 15,000 ($150 USD) per month."
"Product is priced at the market standard."
"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."
"Azure products generally offer competitive pricing, suitable for diverse budget considerations."
"The price you pay is determined by how much you use it."
"The cost is based on the amount of data sets that we are ingesting."
"I would rate Data Factory's pricing nine out of ten."
"I would not say that this product is overly expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
12%
Manufacturing Company
9%
Government
7%
Government
14%
University
11%
Computer Software Company
9%
Retailer
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise19
Large Enterprise55
No data available
 

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...
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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
Team Rubicon, CGI
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