Microsoft Technology Licensing, LLC

Parry Husbands

Tong Wen

Samuel Weiss

Debi P Mishra

Sudarshan Raghunathan

Damon Robert Hachmeister

Andy Linfoot

Implementing static loaders and savers for the transfer of local and distributed data containers to and from storage systems can be difficult because there are so many different configurations of output formats, data containers and storage systems. Described herein is an extensible componentized data transfer framework for performant and scalable authoring of data loaders and data savers. Abstracted local and distributed workflows drive selection of plug-ins that can be composed by the framework into particular local or distributed scenario loaders and savers. Reusability and code sparsity are maximized.

Framework for authoring data loaders and data savers

A platform that provides a way to automatically compose and execute even complex workflows without writing code is described. A set of pre-built functional building blocks can be provided. The building blocks perform data transformation and machine learning functions. The functional blocks have well known plug types. The building blocks can be composed build complex compositions. Input and output files are converted to a standard data type so that modules are pluggable.

Composing and executing workflows made up of functional pluggable building blocks

Growing community of inventors

Waltham, MA, United States of America

Parry Husbands