The solution's pricing is high. Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive. There could possibly be a little bit of overhead concerning the maintainability of the platform.
Computing is very expensive. If you want to create new models on specific data sets, computing that is quite costly. Python's current setup within Palantir is very limiting. I would like to have more freedom to use Python without limitations.
The application development aspect can be improved significantly and would make a difference. We use some third-party tools for reporting and it's a challenge for us to move data into the file system because Palantir is a closed environment and there are difficulties receiving data from external sources. There are some options in place for dealing with that but it's not sufficiently intuitive. I'd like the data exporting functionality to be as intuitive as the importing.
The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points. It was hard to generalize that and put it into a catalog for people who you didn't know to reuse data. Maybe it's different now. That's the nice thing about Informatica: The catalog is reusable. Palantir is successful, and the institution loves them. I don't want to disparage it. I'm just speaking technically from an interoperability perspective.
The workflow could be improved. Although it works rather seamlessly, the workflow too complicated sometimes. Maybe they can reduce the complexity of the workflow. It could be more modularized in the future. The performance of the engine could be better.
Associate - Inhouse Consulting at a pharma/biotech company with 10,001+ employees
Real User
2020-07-12T11:48:52Z
Jul 12, 2020
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.
Palantir Foundry is an enterprise data management platform offering comprehensive tooling for working with big data. Because it is an operating system made for modern enterprises, it is highly available and a continuously updated platform. Palantir Foundry is a fully managed SaaS platform that spans from cloud hosting and data integration to flexible analytics, visualization, model-building, operational decision-making, and decision capture. It equips technical and non-technical users to...
The solution's pricing is high. Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive. There could possibly be a little bit of overhead concerning the maintainability of the platform.
Computing is very expensive. If you want to create new models on specific data sets, computing that is quite costly. Python's current setup within Palantir is very limiting. I would like to have more freedom to use Python without limitations.
The application development aspect can be improved significantly and would make a difference. We use some third-party tools for reporting and it's a challenge for us to move data into the file system because Palantir is a closed environment and there are difficulties receiving data from external sources. There are some options in place for dealing with that but it's not sufficiently intuitive. I'd like the data exporting functionality to be as intuitive as the importing.
The one area where improvement could be made is the cost of the solution which is quite expensive.
The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points. It was hard to generalize that and put it into a catalog for people who you didn't know to reuse data. Maybe it's different now. That's the nice thing about Informatica: The catalog is reusable. Palantir is successful, and the institution loves them. I don't want to disparage it. I'm just speaking technically from an interoperability perspective.
The workflow could be improved. Although it works rather seamlessly, the workflow too complicated sometimes. Maybe they can reduce the complexity of the workflow. It could be more modularized in the future. The performance of the engine could be better.
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.