I didn't use Foundry, but I went through some training, and my team became certified in it. When I left the company, there were probably 100 research projects that had been added to it. I did it project by project. Around 30 were completed. About 40 or 50 were in progress, while there were 20 more in the queue. You could reuse data and leverage data that had been imported. We imported lots of Epic data.

You needed permission to see the Epic data.  Someone with a research project approved by the institution could ask permission to join it with other data. In a relational world, you could say, "I'll give you database permissions, but I'll need to mask these columns that are based on those." It's similar to an SQL database.

People submitted their project requests to a project review committee. The capacity was limited because people needed to understand the platform, but I'm sure they have trained more people on it since then.

We mainly used the solution to ingest our internal data. 

We have a client which uses it for insurance. The insurance company needed to calculate the premiums. In order to do that, we basically get our data from many regions, maybe New Zealand or Australia, Asia, and India. We focus on those regions. 

From those regions, get data in the form of Excel sheets or CSV files. We ingest those formats into the Palantir Foundry platform. And after ingesting, our data scientists upload the stream. Basically, they are trying to calculate the premiums on the basis of the data funneled over to them.

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. 

Palantir Foundry is being used for multiple hybrid cloud integrations in one of the services we provide for an existing US-based customer. It's all about getting together data from Azure and Amazon and then providing a hybrid platform through Palantir Foundry. We then provide the analytics or insights enablement for the customer.

Our primary use case is for data engineering and some data analysis, bringing in data from several sources and using data wrangling and data managing to support the reporting tools we have. We use the reporting apps for some of our basic reporting. We are customers of Palantir. 

Palantir Foundry builds full applications for us. We can build an end-to-end application from data ingestion and data pipelining, to application building.

Early on, we built an internal application that ingested data from our ERP and BI environment that is used by around 700 users. 

I use Palantir to build pipelines and ingest data from external sources like data lakes and bring that data from the data lake to the Palantir environment. Then, I use it to process data using some languages like Spark or SQL. The solution uses Spark for data processing. I can use Spark in the Palantir environment and create reports based on it.

Our company uses the solution to elaborate off PySpark. We run PySpark scripts for our products but use the solution to leverage the workflows that automatically run time bound and trigger bound. When a data trigger is there, the solution executes and the script starts running. 

Our scripts were long so they ran for two to three hours because they were generating some kind of commentary. The solution takes raw data as input, processes it, and converts it into a fine commentary for products.

More than 100 staff use the solution. 

This solution is used more for the analytics available on the platform.

The main use was for a COVID-19 White House initiative that was handled by the Vice President, Michael Pence.

We use Palantir Foundry for data engineering and self-service tools. Palantir is a great service tool for business users who don't have the necessary IT skills. It helps them to easily draw up their own models and use cases with data by simply using Palantir's drag and drop tool. 

It's a great tool for us to say, "Here's your data. You can play around it, build models with it, aggregate tables, and check everything on your own." It's a self-service tool.

It's deployed on cloud. The cloud provider is AWS.

Over 300 people are using this solution in my organization. It's used on a daily basis.

We used it for a utility company for preventative maintenance of physical utility assets such as poles, electrical wires and transformers.

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.

This is a data integration tool with multiple components that link to multiple sources to create repositories, transform data and make it available for dashboards or management purposes. We're based in the UAE and I'm a senior manager, customer and user of this solution.