Amazon Technologies, Inc.

Gregory Richard Swensen

Thomas Lowell Keller

Muhammad Ali Siddiqui

Melissa Elaine Davis

Miles Childs Kaufmann

Amos Dylan Vance

Christopher F Weight

Michael James Panzitta

Xin Xiang

Computer applications may generate event data based on a large volume of different types of record data. Described herein are systems, methods and devices for providing website recommendations using the event data. In one example, using the event data, a computing node generates the website recommendations within a designated amount of time after the generation of the record data.

Systems and methods providing recommendation data

Computer applications may generate a large volume of different types of record data. In one example, the large volume of record data may represent millions of different processes occurring every second. Described herein are systems, methods and devices for generating parsed data based on the large volume of record data. The parsed data may be consumed by computing nodes within a designated amount of time from the generation of the record data.

Systems and methods providing event data

Computer applications may generate event data based on a large volume of different types of record data. Described herein are systems, methods, and devices for enabling a computing node to implement new functions for dynamically consuming the event data. In one example, the computing node may implement a new function using an expression language, without modifying predefined hard coded functions.

Systems and methods providing format data

Various embodiments of a system and method for an order handling data model with item-level granularity are described. Embodiments may include an order handling system configured to, for a given purchase order received from an ordering entity, generate within a data store a purchase order data object, item data objects for items of the purchase order, and message data objects for messages associated with item data objects. In various embodiments, the order handling system may also generate in the data store mapping information for mapping each of the different data objects for a given purchase order together. The order handling system may also provide to the ordering entity a notification for a particular item. The notification may be based on information accessed from the item data object corresponding to the particular item or information accessed from other data objects associated with that item data object according to the mapping information.

System and method for an order handling data model with item-level granularity

Various embodiments of a system and method for order processing state management are described. Embodiments may include an order handling system that includes multiple processing components for performing a respective portion of an order fulfillment process for purchase orders. The order handling system may also include a data store for storing state information of the purchase orders. To process a given purchase order, the order handling system may utilize each processing component to complete a respective portion of the order fulfillment process for that purchase order. The order handling system may also utilize each component to, based on the respective task performed, update state information corresponding to the aforesaid purchase order in the data store. The order handling system may, subsequent to an interruption of the processing of the purchase order, resume the processing of the purchase order based on the updated state information.

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Lynnwood, WA, United States of America

Gregory Richard Swensen