Palantir Metropolis to integrate, model, and analyze data
Palantir Metropolis to integrate, model, and analyze data : The Palantir Metropolis platform is ideal for large-scale quantitative investigation. Palantir Metropolis integrates across multiple sources of data, bringing together disparate information into a unified quantitative analysis environment. The Palantir Metropolis interactive user interface brings abstractions to life in the form of rich visualizations. Tables, scatter plots, and charts interact seamlessly to provide a holistic view of all integrated data of interest. The visualizations update in real-time with the source data, so users always see the most accurate and current information at any given time.
Palantir Metropolis is built for rapid iteration and collaboration.Analysts can tweak their logic, test new hypotheses, and present new findings to decision-makers who review the analysis, update their priors, and ask new questions. The enterprise gets smarter, and the cycle continues. In Palantir Metropolis, data models are the basic building blocks of analysis. Models are the translation of the rows and columns of source datasets, including descriptive metadata, into a unified conceptual object that represents an entity in the world. A model can be an organization, a company, a person—any real world object described by the data. Each installation of Palantir Metropolis is configured with the types of models necessary to answer the questions at hand.
Hedgehog Language, or HHLang, is a scripting language with syntax similar to Java that was specifically developed to facilitate complex analysis in Palantir Metropolis—models, metrics, and documents are all first-class language constructs in HHLang. With language features such as a proper type system, expression chaining, anonymous functions / lambdas, and collections, HHLang allows analysts to describe both simple expressions and complex, multi-module calculations.
Palantir Metropolis is designed from the ground up to be extensible at every layer of the stack. From low-level data integration, custom metrics, to building custom user interface to implement specific workflows, it has been designed as a fundamentally open platform.