Now that you are oriented in the Palantir platform and understand its core concepts, you can explore the platform capabilities that are most relevant for your role.
The boundaries between roles in the Palantir platform can shift, and some responsibilities and workflows may not align perfectly into a single role. You may end up performing different roles; if your organization is just getting started with Palantir, or if you are working on a small implementation team, you may use many parts of the platform in your day-to-day work.
With this in mind, the following roles are generally adopted at most organizations using Palantir. Below, we discuss these high-level roles and how each type of user can get started:
Alternatively, you can explore the Palantir platform by reviewing the application reference, which provides a high-level overview of the major platform applications.
Palantir's data integration layer provides the foundation for all the other work that happens in the platform. By building and maintaining data pipelines, data engineers produce datasets that are high-quality, relevant, and frequently updated to serve the needs of the organization. A wide variety of tools are available to maintain the durability of data pipelines over time, including programmatic health checks and transparency into the underlying computation.
The primary tools used by data engineers include Pipeline Builder and Code Repositories for authoring data pipelines, and Data Lineage for visualizing them end-to-end. Data engineers will need to be familiar with the concept of data pipelines and develop an understanding of what makes for a high-quality pipeline in the platform.
Learn more about data pipelines.
Palantir's Ontology and application building capabilities enable you to rapidly create tailored, high-quality applications for end users. These end users are usually operators in an organization, making decisions that can be empowered using data. Beyond just presenting data to users, you can use custom applications to capture information from users in the form of action types configured in the Ontology.
Application builders will need to be familiar with the Palantir Ontology, which is usually the layer at which application builders collaborate with data engineers to establish a data foundation for workflow development. Builders can create and maintain their organization's Ontology in the Ontology Manager, and explore objects and related workflows with Object Explorer and Object Views. Application builders can create alerts triggered by changes to the Ontology with Automate.
To create and deliver applications, builders can use Workshop, Palantir's product for point-and-click application building on top of the Ontology. To develop using code, application builders may write Functions to define business logic used across applications, or create applications in Slate, Palantir's framework for application development using HTML, CSS, and JavaScript.
Learn more about application building.
The Palantir platform includes support for analyzing data using code and developing, evaluating, and deploying machine learning models. This functionality builds on top of the rigor of the data integration layer to provide lineage and reproducibility for models in the same way as datasets. The result is an environment where analytics and machine learning are accelerated by high-quality data and rapid time-to-value for modeling projects.
In the Palantir platform, data scientists often use Code Workbook, an application designed to enable code-based analysis and the development of machine learning models. Code Workbook enables you to write code in Python, R, and SQL to access, normalize, and analyze high-quality datasets prepared by data engineers. The resulting analyses and models can then be integrated into the Ontology to unlock capabilities like deploying models directly to end users through Palantir's application building frameworks.
As an alternative, data scientists can work in their preferred third-party IDEs with Code Workspaces. Code Workspaces containers are natively integrated with the rest of the Palantir ecosystem to combine JupyterLab® and RStudio® Workbench IDEs with the security, branching, and resource management benefits of the Palantir platform.
Learn more about model integration and code-based analysis.
As Palantir can be used to build a secure and high-quality data foundation, analysts can quickly find and explore data that is relevant to the questions they need to answer. A rich set of tools is available for analyzing data in a wide variety of formats—tabular, relational, temporal, geospatial, and more. Once your analysis yields insight, you can make it repeatable by creating dashboards or present your findings using reporting tools.
Analysts typically use Contour to explore datasets in the platform and conduct open-ended analysis at high-scale, and use Quiver to analyze data in the Ontology, along with associated time series. Both applications support converting ad-hoc analyses into dashboards, and support embedding findings into Notepad documents to share results with colleagues.
Platform administrators can use Palantir's dedicated administrative tooling to configure the platform, manage and understand how it is being used, and ensure that the organization's data is being managed securely.
Platform administrators typically set up authentication to connect to an organization's identity provider, then set up Data Connection to enable data to flow into the platform. As use of the platform matures, administrators can use Resource Management to manage resource consumption and ensure data is secured properly.
Learn more about platform administration.
Palantir provides best-in-class tools for securing data and providing transparency to data governance leads. These tools provide guarantees about how data is protected as it is transformed in the Palantir platform and used for user-facing applications, while preserving the ability for you to introspect and validate who has access to what information.
Users in data governance roles should learn about the broad set of data security workflows in the platform, ranging from securing a data foundation to protecting sensitive data. These capabilities are built on Palantir's unique data security concepts, namely Projects and Markings.