Palantir Foundry Reviews

Our company uses the solution as a big data lake for storage and cherry-picking data sets using multiple languages. We create ETL pipelines, run them on a schedule, and export the data to visualize it. 

We perform functional tests on the data sets using Excel in a Fusion Sheet. A schema is created that shows all data in columns and can be manipulated to extract meaningful information. 

The Code Workbook is used to import data and write code using R, Python, Spark SQL, or PySpark. From there, you can perform calculations and create data sets. 

Contour is the graphical user interface that gives us the available basic or automatic operations. You do not need a technical grasp because it is easy to use with knowledge of the basics and filters. 

Across our company, there are 3,000 users who access our data lake. 

The Code Workbook gives you the option to switch across built-in languages such as Spark SQL, PySpark, R, or Python. 

Live video sessions enhance the available documentation and allow you to ask questions directly. There are a multitude of sessions within each framework that occur weekly. At the end of a session, you have the option to read other user's questions or ask questions yourself. 

The GUI is easy to use and does not require advanced technical knowledge. 

There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers. It is easy to find answers for code issues because Spark SQL and Python have wide user bases. There is a certain probability you won't find a solution-specific answer if you search for it. For example, there are certain errors that are specific to the solution. The more you use the solution, the more you understand it. The learning curve could be reduced with online documentation that includes the meaning of and troubleshooting for error codes. 

Predefined code templates or informational prompts would help with writing syntaxes. 

I have been using the solution for fourteen months. 

The solution is very stable. On occasion, we receive an error but it is rectified within a few hours. 

We create use cases that do not have processing limits. The solution is a big data tool so should handle any scalability. 

I have not needed technical support. 

I previously used Microsoft SQL which is a traditional database. The solution is an advancement because it is a direct jump to a big data source. 

Comparing the solution to traditional databases is liking comparing an apple to a banana.

A different team handles the solution and our data lake so I don't have knowledge of the setup. Our team accesses the solution via a web link and creates use cases. 

The solution has many features but I am only using the Code Workbook and Contour. Another feature called Slate allows you to create websites or record user data. 

Based on my current usage, I rate the solution a seven out of ten. 

We use this solution for everything, including sales. One of our use cases is performing machine learning to gives us an understanding of customer behavior, and which message should be used to target different customers.

The interface is really user-friendly.

This product allows you to do a better data governance job. For example, it will show you data along with information that explains what it is about and what columns it comes from. It will also allow you to give row-level access to different people, which is important in a corporate setting because restricting access to certain datasets is important.

The built-in automation makes tasks easier.

This solution allows you to do an extensive analysis that cannot be done on a personal computer, or even an on-premises server because of the computational effort that is required. It is enormous. 

They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure.

There are some issues with scalability because when we are using a really large dataset, the system is rather slow.

The performance can be improved. It would make our life a lot easier if it were as fast as Google Cloud. The GCP is unmatchable in terms of the speed at the moment.

From a user perspective, it would be nice to have a preview of what the data is looking like. As it is now, you can see the schema but not the actual data. For example, they can see the different columns but they don't know what's there. If they could inspect the first few hundred columns of data then they would have an idea of what they are dealing with.

I have been using Palantir Foundry for the past two years.

This solution is scalable to an extent. Because we have to rely on different cloud providers, the performance when scaling depends on your negotiations with the providers. If there is enough bandwidth then it works fine but if not, it tends to get quite slow at times.

Technical support is okay.

I would rate this solution an eight out of ten.