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Palantir Foundry vs Skyvia comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Mar 1, 2026

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

Palantir Foundry
Ranking in Data Integration
3rd
Ranking in Cloud Data Integration
5th
Average Rating
8.0
Reviews Sentiment
6.4
Number of Reviews
62
Ranking in other categories
IT Operations Analytics (4th), Supply Chain Analytics (1st), Data Migration Appliances (3rd), Data Management Platforms (DMP) (1st), Data and Analytics Service Providers (1st)
Skyvia
Ranking in Data Integration
63rd
Ranking in Cloud Data Integration
29th
Average Rating
9.0
Reviews Sentiment
7.8
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2026, in the Cloud Data Integration category, the mindshare of Palantir Foundry is 3.9%, down from 5.7% compared to the previous year. The mindshare of Skyvia is 1.5%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Cloud Data Integration Mindshare Distribution
ProductMindshare (%)
Palantir Foundry3.9%
Skyvia1.5%
Other94.6%
Cloud Data Integration
 

Featured Reviews

reviewer2846265 - PeerSpot reviewer
PALANTIR DATA ENGINEER at a healthcare company with 10,001+ employees
Unified healthcare pipelines have improved data trust and accelerated operational decisions
One challenge regarding how Palantir Foundry can be improved is the learning curve. Foundry has a very broad ecosystem with Ontology, Pipeline Builder, Code Repositories, and AI integrations. For new engineers or business users onboarding, it can take time, especially if they are coming from more traditional data platforms. Better documentation, simplified onboarding paths, and more beginner-friendly examples would help accelerate adoption. Another area is debugging complexity. While lineage and monitoring are strong features, troubleshooting deeply interconnected pipelines can still become difficult in a large enterprise environment. Sometimes error logs and pipeline failure messages could be more descriptive or developer-friendly, especially for distributed PySpark jobs. Another pain point is customization limitations in certain UI-driven components. While low-code tools are great for rapid development, highly customized workflows sometimes still require engineering workarounds or deeper technical implementation. The platform is extremely capable, but improvements around usability, debugging experience, DevOps flexibility, and ecosystem openness would make it even more effective for enterprise engineering teams.
RH
CTO & Developer at a consultancy with self employed
The product works, is simple to use, and is reliable.
Error handling. This has caused me many problems in the past. When an error occurs, the event on the connection that is called does not seem to behave as documented. If I attempt a retry or opt not to display an error dialog, it does it anyway. In all fairness, I have never reported this. I think it is more important that a unique error code is passed to the error event that identifies a uniform type of error that occurred, such as ecDisconnect, eoInvalidField. It is very hard to find what any of the error codes currently passed actually mean. A list would be great for each database engine. Trying to catch an exception without displaying the UniDAC error message is impossible, no matter how you modify the parameters in the OnError of the TUniConnection object. I have already implemented the following things myself. They are suggestions rather than specific requests. Copy Datasets: This contains an abundance of redundant options. I think that a facility to copy one dataset to another in a single call would be handy. Redundancy: I am currently working on this. I have extended the TUniConnection to have an additional property called FallbackConnection. If the TUniConnection goes offline, the connection attempts to connect the FallbackConnection. If successful, it then sets the Connection properties of all live UniDatasets in the app to the FallbackConnection and re-opens them if necessary. The extended TUniConnection holds a list of datasets that were created. Each dataset is responsible for registering itself with the connection. This is a highly specific feature. It supports an offline mode that is found in mission critical/point of sale solutions. I have never seen it implement before in any DACs, but I think it is a really unique feature with a big impact. Dataset to JSON/XML: A ToSql function on a dataset that creates a full SQL Text statement with all parameters converted to text (excluding blobs) and included in the returned string. Extended TUniScript:- TMyUniScript allows me to add lines of text to a script using the normal dataset functions, Script.Append, Script.FieldByName(‘xxx’).AsString := ‘yyy’, Script.AddToScript and finally Script.Post, then Script.Commit. The AddToScript builds the SQL text statement and appends it to the script using #e above. Record Size Calculation. It would be great if UniDac could estimate the size of a particular record from a query or table. This could be used to automatically set the packet fetch/request count based on the size of the Ethernet packets on the local area network. This I believe would increase performance and reduce network traffic for returning larger datasets. I am aware that this would also be a unique feature to UniDac but would gain a massive performance enhancement. I would suggest setting the packet size on the TUniConnection which would effect all linked datasets.

Quotes from Members

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

Pros

"In terms of improvements, it helped us improve our data migration timelines by approximately 60 percent and improved the data accuracy and addressed the issues upfront by approximately 85 percent."
"After we automated the entire workflow on Palantir Foundry, it ran really well in terms of time and correctness, and also data quality."
"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."
"Palantir Foundry has improved with the generation of AI, and I think the governance and security are both good things to have in Palantir Foundry."
"The virtualization tool is useful."
"The AI engine that comes with Palantir Foundry is quite interesting."
"I like the data onboarding to Palantir Foundry and ETL creation."
"I appreciate multiple aspects of Palantir Foundry, starting with the clean architecture and clean UI, and I really value how easily I can create and test Python and PySpark scripts, trace data lineage to debug issues, monitor daily pipelines and health checks, and quickly build very interactive Workshop applications, all supported by a clean and informative Resource Management UI that helps track costs and data usage."
"For what it offers, I think this solution is a must for any Delphi programmer."
 

Cons

"It requires a lot of manual work and is very time-consuming to get to a functional point."
"The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has."
"I believe that the AI or agent needs improvement because sometimes we face difficulties when looking for solutions, and when we ask the agent, AIP, it does not understand our queries and occasionally provides wrong solutions."
"There are some limitations when dealing with a large-scale project."
"There is a learning curve to Palantir Foundry, and better documentation would really help because while documentation exists, not everything is covered."
"This system needs more powerful tools for the power user; I feel the system is very well designed for the introductory level but could have finer-grained controls for data engineering experts and machine learning experts at the power user level."
"I cannot advise someone to use Palantir Foundry due to cost efficiency and the complexity it introduces in handling large amounts of data."
"The solution could use more online documentation for new users."
"Error handling has caused me many problems in the past; when an error occurs, the event on the connection that is called does not seem to behave as documented."
 

Pricing and Cost Advice

"The solution’s pricing is high."
"Palantir Foundry has different pricing models that can be negotiated."
"It's expensive."
"Palantir Foundry is an expensive solution."
Information not available
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Top Industries

By visitors reading reviews
Manufacturing Company
14%
Financial Services Firm
10%
Comms Service Provider
7%
Government
6%
Construction Company
15%
Performing Arts
15%
Comms Service Provider
9%
Outsourcing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business11
Midsize Enterprise7
Large Enterprise51
No data available
 

Questions from the Community

What needs improvement with Palantir Foundry?
The Workshop application could be improved because it is not very customizable, but it is still very strong. We also have the React OSK apps, but it does not allow the inbuilt applications such as ...
What is your primary use case for Palantir Foundry?
My main use case for Palantir Foundry is to solve business problems, such as in healthcare. I also worked on a project for a law firm where thousands of PDFs were coming in, and we needed to check ...
What advice do you have for others considering Palantir Foundry?
I believe they should get started by completing all the free certificates, then they could apply to the paid certificates to get a master of Palantir Foundry, solve some real use cases, do examples...
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Also Known As

No data available
Skyvia, Skyvia Data Integration
 

Overview

 

Sample Customers

Merck KGaA, Airbus, Ferrari,United States Intelligence Community, United States Department of Defense
Boeing, Sony, Honda, Oracle, BMW, Samsung
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