You can interact with the Palantir platform using applications accessible via the sidebar. This page provides a reference for the range of applications available and describes when you may want to use each one.
| Application | Description | Use |
|---|---|---|
| Data Lineage | Data Lineage shows a graph of how data flows through the platform. | Explore the origins or downstream usage of any data in the Palantir platform. |
| Pipeline Builder | Pipeline Builder creates end-to-end pipelines from data sources to final outputs using built-in data transformations. | Integrate data towards analysis and application building with batch and streaming pipelines. |
| Code Repositories [1] [2] | Code Repositories is a web-based code authoring environment with support for versioning and collaboration. | Create data pipelines or write Functions in the Ontology. |
| Dataset Preview | Dataset Preview shows the contents and history of a dataset. | Browse a dataset and understand its history and other metadata. |
| Data Health | Data Health lets you define health checks to ensure datasets are high-quality. | Add or monitor health checks on datasets. |
| Data Connection | Data Connection allows you to connect to data sources and sync data into the Palantir platform. | Connect to organizational data sources or sync new datasets into the Palantir platform. |
| HyperAuto (SDDI) | HyperAuto generates end-to-end data pipelines on top of common ERP systems. | Generate an Ontology from enterprise systems without needing to develop pipelines manually. |
[1] Code Workbook or Code Workspaces may be more suitable for certain data science workflows. Learn more about the difference between Code Workbook, Code Workspaces, and Code Repositories.
[2] Pipeline Builder may be a better fit if you are a less-technical user.
| Application | Description | Use |
|---|---|---|
| Model Assets | Model Assets enable integration of many different model types into the Palantir platform. | Train models, and connect to externally hosted models in the Palantir platform. |
| Modeling objectives | A modeling objective allows organizational stakeholders and model developers to collaborate on and deploy machine learning models. | Submit models; discuss modeling objectives, and deploy models into production. |
| Application | Description | Use |
|---|---|---|
| Ontology Manager | Ontology Manager enables you to define your organization's Ontology. | Create new object, link, and action types. |
| Object Views | Object Views represent the canonical way to display an object type. | Define user interfaces that can be used across use cases. |
| Object Explorer | Object Explorer allows you to search and visualize your Ontology. | Search and analyze objects and links in the Ontology. |
| Vertex | Vertex enables you to explore object relationships and run simulations. | Create system graphs of related objects and run end-to-end simulations using models. |
| Automate | Automate allows end users and application builders to see when data changes in the Palantir Ontology. | Configure automations to send notifications or submit Actions when certain conditions are met. |
| Foundry Rules | Foundry Rules enables users to actively manage complex business logic in the platform. | Create and apply rules to datasets, objects, and time series for a variety of use cases. |
| Map | Map provides powerful geospatial and temporal analysis and visualization capabilities. | Integrate data from across the platform into a cohesive geospatial experience. |
| Application | Description | Use |
|---|---|---|
| Workshop [1] | Workshop enables the creation of interactive and high-quality applications for end users. | Create apps using data in the Ontology in a rapid, point-and-click interface. |
| Slate [2] | Slate is an extensible application development framework. | Create a customized application using HTML, CSS, and JavaScript. |
| Carbon | Carbon lets you combine apps and other resources in the platform to create curated workspaces for end users. | Deliver a use case to end users that combines multiple applications or dashboards. |
[1] Slate may be a better fit if heavy customization is needed for your application.
[2] Workshop is a better fit for applications of low to moderate complexity, and generally poses a lower maintenance cost over time.
Learn more about analytical applications and the available types of analysis in the platform.
| Application | Description | Use |
|---|---|---|
| Contour [1] | Contour enables high-scale, top-down analysis on datasets. | To analyze tabular data in a point-and-click fashion. |
| Quiver [2] | Quiver enables analysis on object data and time series. | Analyze Ontology data and time series in a point-and-click fashion. |
| Code Workbook [3] | Code Workbook is a web-based environment for code-based analysis. | Analyze datasets in code, conduct data science workflows, or develop models. |
| Code Workspaces [3] | Code Workspaces brings the JupyterLab® and RStudio® Workbench third-party IDEs to Palantir. | Boost productivity and accelerate data science and statistics workflows with preferred tools on the high-quality data of the Palantir Ontology. |
| Notepad | Notepad enables creating point-in-time documents that present data for sharing with others. | Present insights from analytical workflows. |
| Fusion | Fusion is a spreadsheet application for the Palantir platform. | Sync data from an editable spreadsheet to a dataset. |
[1] Quiver may be a better fit for some workflows. Learn more.
[2] Contour may be a better fit for some workflows. Learn more.
[3] Code Repositories and Pipeline Builder are recommended for developing production data pipelines. Learn more about Pipeline Builder and the difference between Code Workbook, Code Workspaces, and Code Repositories.