I've heard people say it's basically SAP with some spooky mystique for marketing.
[not entirely true note: PalantirFoundry is a lowCode/noCode stack on top of a Spark cluster]
Pipeline Builder : https://youtu.be/WIQA7u1tbsY?si=iG-N9xHpUPPVAqDX
OntologyManager: https://youtu.be/SOW0IA_I0bk?si=u5eha0o-yrn4pHvu
These are slogans with marketing grade truth, not engineering truth. The moment you leave the happy path wizards you run straight into Python, SQL, JVM knobs, and cluster limits. The visual Pipeline Builder drops you into a Code node the second you need anything beyond basic joins/filters. Here is an example: https://www.palantir.com/docs/foundry/code-workbook/getting-...
>> nor scalability consideration. Foundry Spark jobs expose tunable variables, you are expected to tweak them for performance and cost: https://www.palantir.com/docs/foundry/optimizing-pipelines/s...
Having most of those PySpark functions exposed visually as building blocks with debug points available at each step simply outperforms a codeWorkbook in term of maintainability and readability.
...Except in this case, there might be a lot of value in letting James Bond or Boris Badenov whip up a quick spy pipeline without putting up a job ad for a Python coder.
This is marketing speak who means nothing. What do you mean? Something like this?: https://www.ibm.com/products/information-governance-catalog
Here is a tool to visually explore your graph:
https://www.palantir.com/docs/foundry/vertex/graphs-explore
And here is the search engine to search for your objects (free-text and/or types): https://www.palantir.com/docs/foundry/object-explorer/gettin...
And here is the tool to manage the ontology (the types and relationships of the graph, plus the mapping of the graph to the tabular datasets):
https://www.palantir.com/docs/foundry/ontology-manager/overv...
The UI designer based on the graph data is called Workshop:
https://www.palantir.com/docs/foundry/workshop/overview/
I still have to find a stack where this duality is as integrated as in Foundry.
This one has 10 years of precedence: https://www.ibm.com/products/information-server
All of these things are pretty fascinating, as they concern the meta of the meta; the nature of knowledge itself; how to slice the ineffable reality into parts and how to communicate complex things to people.
Many software engineers are happy not knowing any of those terms. This perceived boredom is part of the reason why companies, some indeed with questionable ethics, like Palantir (is there even more than one in this space?) have very little competition and are making big bucks on big contracts (and only stand to make more once ML becomes less chaotic and more structured).
Two comments here:
- the real power of Foundry is that it is REALLY scalable. Palantir is selling all the knowledge management+data management that you mentionned BUT at a level of integration and scalability that I had never seen in any other product.
- someone in the discussions mentionned IBM InformationServer as an alternative. Functionnaly that sounds quite similar to Foundry. The question is whether the implementation choices are as clever as in Foundry. #toBeInvestigated
So, lots and lots of pie charts?
Linux. Windows. Both are "operating systems"?
What actual novel data collating and anomaly detection algorithms does Palantir implement that no one else does? Oh that's right these services are all just different corporate branding atop the same old open source stacks and libraries everyone uses.
Show me a entity-relationship model management [with version control] that wraps Spark data frames into a fully ontology-based graph database.
Show me a low code/no code UI designer that is fully consuming the ontology layer abstracting your Spark dataFrames.
#meWaiting
They exist but are used in house not sold as products.
There are information theory texts with all these ideas written in them published back in the 1950s we just didn't have powerful enough computers
Thiel and Musk's only advantage is age. They were able to implement first due to a single property of physics.
Most "geniuses" today, like Tao, just like reading math. None of them have any truly novel net new ideas. They're parrots whose cages were in the nerdy section of a book store.