Azure Data Factory vs Palantir Gotham comparison

Microsoft and Palantir are both solutions in the Data Integration category. Microsoft is ranked #3 with an average rating of 8.2, while Palantir is ranked #50. Microsoft holds a 2.8% mindshare in DI, compared to Palantir’s 0.8% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Palantir users who would recommend it.

Azure Data Factory

Read 94 Azure Data Factory reviews

10,752 Views
7,526 Comparison Views

92% willing to recommend

Palantir Gotham

Read 1 Palantir Gotham review

1,130 Views
1,017 Comparison Views

100% willing to recommend

Azure Data Factory

Palantir Gotham

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and Palantir Gotham based on real PeerSpot user reviews.

Find out what your peers are saying about Microsoft, Informatica, Qlik and others in Data Integration.

To learn more, read our detailed Data Integration Report (Updated: March 2026).

Buyer's Guide

Data Integration

March 2026

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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

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

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.8%, down from 9.7% 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 Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.8%
Palantir Gotham	0.8%
Other	96.4%

Data Integration

Featured Reviews

KandaswamyMuthukrishnan

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.

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Wallace Hugh

Manager at a tech services company with 201-500 employees

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.

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Quotes from Members

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

Pros

"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."

"Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time and hours of repeated manual work."

"The solution is okay."

"The most valuable part of this product is the ease of use, as it is easy to use and rather intuitive, and because it is easy to use, you can do things with it easily, making your work easier and therefore more valuable."

"The most valuable feature is the copy activity."

"The overall architecture has been very valuable to us, as it has allowed us to scale up pretty rapidly."

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS."

"The reason that we implemented this product is for the full integration with the whole Azure environment."

More Azure Data Factory pros

"This solution is seamless. From one platform, we can do just about anything."

"This solution is seamless; from one platform, we can do just about anything."

Cons

"There's space for improvement in the development process of the data pipelines."

"Data Factory's cost is too high."

"The pricing model should be more transparent and available online."

"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."

"I find that Azure Data Factory is still maturing, so there are issues."

"There is no built-in pipeline exit activity when encountering an error."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy."

"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."

More Azure Data Factory cons

"I think there should be less coding involved. Currently, using it involves a tremendous amount of coding."

Pricing and Cost Advice

"Pricing appears to be reasonable in my opinion."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"I don't see a cost; it appears to be included in general support."

"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 model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"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."

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

11%

Manufacturing Company

Government

13%

Computer Software Company

10%

Retailer

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	20
Large Enterprise	57

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.

See all answers

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...

See all answers

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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Comparisons

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 5% of the time

Snowflake vs Azure Data Factory

Compared 5% of the time

Palantir Foundry vs Azure Data Factory

Compared 4% of the time

FME vs Azure Data Factory

Compared 4% of the time

Oracle Data Integrator (ODI) vs Azure Data Factory

Compared 3% of the time

More Azure Data Factory Competitors

Palantir Foundry vs Palantir Gotham

Compared 39% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Palantir Gotham

Compared 9% of the time

Collibra Platform vs Palantir Gotham

Compared 8% of the time

SSIS vs Palantir Gotham

Compared 7% of the time

FME vs Palantir Gotham

Compared 7% of the time

More Palantir Gotham Competitors

Product Reports

Buyer's Guide

Azure Data Factory

March 2026

Download Azure Data Factory product report

Buyer's Guide

Data Integration

March 2026

Download Palantir Gotham product report

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

The Palantir Gotham platform comprises a suite of capabilities for integrating many different data sources for secure, collaborative analysis. The platform serves as an enterprise knowledge base, containing the full record of an organization's collective analysis.

Palantir

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

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Find out what your peers are saying about Microsoft, Informatica, Qlik and others in Data Integration. Updated: March 2026.

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