Databricks Inc.

Stephen Michael Mahoney

Abhijit Chakankar

Itai Shlomo Weiss

William Chau

Daniel Seth Morris

A data processing service facilitates the creation and processing of data processing pipelines that process data processing jobs defined with respect to a set of tasks in a sequence and with data dependencies associated with each separate task such that the output from one task is used as input for a subsequent task. In various embodiments, the set of tasks include at least one cleanroom task that is executed in a cleanroom station and at least one non-cleanroom task executed in an execution environment of a user where each task is configured to read one or more input datasets and transform the one or more input datasets into one or more output datasets.

Clean room generation for data collaboration and executing clean room task in data processing pipeline

A data processing service receives a request from a first collaborator to create a clean room for data sharing collaboration with at least a second collaborator. In response, the data processing service creates an execution environment separate from the data environment of the first collaborator and the second collaborator. The first and second collaborators can then add content into the clean room in the form of data tables and executable notebooks. Approval from each collaborator is required before a notebook can be executed using any data table shared into the clean room. Upon receiving notebook approval from each collaborator, the data processing service creates a notebook job to execute the notebook on one or more cluster computing resources of the data processing service to generate an output.

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Portland, OR, United States of America

Stephen Michael Mahoney