Palantir Technologies Inc.

Gregory O'Connor

Matthew Fedderly

Joel Ossher

Timothy James Slatcher

Lucas Ray

John McKinstry Doyle

Alessandro Mingione

Justin Streufert

Stephen Yazicioglu

Hunter Pitelka

Henry Tung

Brandon Marc-Aurele

Robert Giardina

Eric Jeney

Christopher Luck

Stefan Negrus

Ethan Lozano

Olivia Zhu

Howard Schindel

Christopher Leech

Jason Lee

William Rhyne

Thomas Haynes

Samuel Fendell

Ethan Breder

Michael Kodiak

Antoine Llorca

Mark Elliot

Adam Borochoff

Benjamin Duffield

Punyashloka Biswal

Mark Schafer

Walker Burgin

Matthew Lynch

Caitlin Colgrove

Matthew Sills

Sander Kromwijk

Stephanie Yu

Gabrielle Javitt

Nir Ackner

Brandon Krieger

Alexandru Antihi

Samuel Szuflita

Phillip Chen

Diran Li

Helen Kaltegaertner

Shannon Scott

Maria Franco

The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The data analysis system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow. The data analysis system further provides data editing, reporting, enrichment, expression language, and charting functionality.

Interactive user interface for dynamically updating data and data analysis and query processing

An approach for transforming a large dataset using user interface-based transformations applied to a sample of the dataset is disclosed. The sample of the large dataset has the same or similar format as the large dataset. A user can quickly apply transformations to the sample dataset using UI-based instructions. The UI-based instructions can be used to create a transformation job that can be configured to run on a backed database, such as a distributed database, to apply the transformations to the large dataset.

User interface data sample transformer

Computer implemented systems and methods are disclosed for importing data from electronic data files. In accordance with some embodiments, a file format is assigned to a source electronic data files by a data importation system. The data importation system may further identify a file type identifier associated with the source electronic data file and map the source electronic data file to a transformation template. The data importation system may further store the file format, file type identifier, and an indication of the transformation template as a file type profile associated with the source electronic data file in a database.

Systems and methods for importing data from electronic data files

A text-based interactive interpreter and user interface that sequentially allows a user to modify, transform, or filter data sets from a database system. Execution of user generated instructions results in output that can be rendered as a table, map, JSON, or other view. A user can easily retrieve a data set from a resource identifier for the data set. Instructions and results are presented in a sequential manner down the user interface page. Instructions can refer to variables and output data from previous blocks in the user interface page. The interpreted user-generated textual instruction areas can retrieve a data set specified in the textual blocks, parse and execute the textual instructions to transform, filter, or manipulate the data set. The resulting data set is rendered according to the data type, default settings, or preconfigured preferences into a table, map, JSON, or other view. Each or a subset of the textual instruction blocks can be persisted, versioned, and permissioned according to access control lists. The particular session of textual instruction blocks and transformations can be published as a service, which, if called again, can dynamically perform the user generated instructions and output the result data.

Interactive interpreter and graphical user interface

Computer implemented systems and methods are disclosed for importing data from electronic data files. In accordance with some embodiments, source electronic data files are received at a data importation system and managed by the data importation system. The data importation system may load detector/transformer plugins and determined whether any of the load detector/transformer plugins apply to the received source electronic data files. The data importation system may also generate transformed data using a transformer corresponding to a detector that applies to the source electronic data files. The data importation system may import the transformed data into at least one of a data analysis system and a database.

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San Francisco, CA, United States of America

Gregory O'Connor