Azure Data Factory vs Palantir Gotham comparison

Microsoft and Palantir are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.1, while Palantir is ranked #50. Microsoft holds a 11.0% mindshare in DI, compared to Palantir’s 0.5% 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 86 Azure Data Factory reviews

21,615 Views
17,232 Comparison Views

92% willing to recommend

Palantir Gotham

Read 1 Palantir Gotham review

1,152 Views
802 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, Talend and others in Data Integration.

To learn more, read our detailed Data Integration Report (Updated: December 2024).

Buyer's Guide

Data Integration

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

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 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 Gotham is 0.5%, down from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

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.

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Wallace Hugh

VP of Data and Analytics 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

"I like the basic features like the data-based pipelines."

"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."

"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."

"The function of the solution is great."

"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."

"We use the solution to move data from on-premises to the cloud."

"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

More Azure Data Factory pros

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

Cons

"The support and the documentation can be improved."

"The thing we missed most was data update, but this is now available as of two weeks ago."

"Azure Data Factory uses many resources and has issues with parallel workflows."

"The pricing scheme is very complex and difficult to understand."

"It can improve from the perspective of active logging. It can provide active logging information."

"We have experienced some issues with the integration. This is an area that needs improvement."

"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."

"The product integration with advanced coding options could cater to users needing more customization."

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 is comparable, it's somewhere in the middle."

"ADF is cheaper compared to AWS."

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

"Data Factory is affordable."

"The cost is based on the amount of data sets that we are ingesting."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"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 price you pay is determined by how much you use it."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Government

13%

Financial Services Firm

11%

Computer Software Company

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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...

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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 PowerCenter vs Azure Data Factory

Compared 13% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

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

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

Palantir Foundry vs Palantir Gotham

Compared 100% of the time

More Palantir Gotham Competitors

Product Reports

Buyer's Guide

Azure Data Factory

December 2024

Download Azure Data Factory product report

Buyer's Guide

Data Integration

December 2024

Download Palantir Gotham product report

Learn More

Microsoft

Palantir

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.

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.

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

Find out what your peers are saying about Microsoft, Informatica, Talend and others in Data Integration. Updated: December 2024.

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