Amazon Technologies, Inc.

Miles Childs Kaufmann

Muhammad Ali Siddiqui

Bhavesh Anil Doshi

Arvinth Ravi

Phillip H Pruett, Iv

Anand Chakraborty

Suresh Kumar Golconda

Nikhil Menon

Shikha Sukumaran

Leon Thrane

Kendra Anne Yourtee

Amos Dylan Vance

Thomas Lowell Keller

Melissa Elaine Davis

Sanjeev Kewal Verma

Arthur Carré

Gregory Richard Swensen

Mathias G Ricken

Aaron T Olds

Alexander S Borst

Thayn Moore

Sneha Parthasarathy Iyengar

Pratik Varshney

Matthew Thomas Sidley

Jiashi Chen

Abhay Bhagirath Murarka

Jessie E Felix

Andrew W Hutton

An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Smart data storage tiers for data object transitioning

An object-based data storage service receives a request to store a data object in a first location corresponding to a first data storage tier. The request may specify a parameter to enable transitioning of the data object to another data storage tier. In response to the request, the object-based data storage service stores the data object in the first location and monitors access of the data object to determine usage data associated with the data object. The object-based data storage service processes the usage data to determine that the data object is to be transitioned to a second data storage tier. As a result of this determination, the object-based data storage service transitions the data object to a second location corresponding to the second data storage tier.

Automated tier-based transitioning for data objects

An indication may be received, by a storage service, from an account, of at least a first object and a first threshold time duration, wherein the first object is to be automatically transferred from a lower-latency storage tier to a higher-latency storage tier based on object access history data and the first threshold time duration. A first determined time duration since a most recent access of the first object may be determined based on the object access history data. The first determined time duration may be compared to the first threshold time duration. It may be determined that the first determined time duration meets or exceeds the first threshold time duration. The first object may be transferred from the lower-latency storage tier to the higher-latency storage tier based on the first determined time duration meeting or exceeding the first threshold time duration.

Automatic object archiving based on user selections

Smart data storage hers for data object transitioning

Systems and methods are provided for analyzing operating metrics of monitored metric sources. Aspects of the present disclosure may present for display information associated with the monitored metric source and the analysis of its operating metrics. Analysis comprises determination of reference values and tolerance levels which represent allowable deviations from the reference values. Input data includes a measurement of an operating parameter and a time stamp. Input data may be saved to a data store for using in future analysis of other input data. When input data is determined to be outside the tolerance level, notifications may be issued to alert administrators or systems of the anomaly.

Integration based anomaly detection service

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

Technologies are described herein for incorporating streaming and/or sampling in real-time log analysis. Representative samples of log data are extracted from the log files on a number of monitored hosts and streamed in real-time to log processors for processing. The log processors accumulate and process the representative samples of log data, and track a data completeness value representing an indication of a proportion of total log data represented by the representative samples received. The representative samples of log data are merged and collated. Estimated metrics are calculated from the merged and collated representative samples and the data completeness, and the estimated metrics are published to consumers in near real-time.

Streaming and sampling in real-time log analysis

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Seattle, WA, United States of America

Miles Childs Kaufmann