Splunk Inc.

Cisco Technology, Inc.

Karthikeyan Ramasamy

Sanjeev Raghavendra Kulkarni

Boyang Peng

Poornima Devaraj

Matteo Merli

Ram Sriharsha

Gaurav Chawla

Mehul Goyal

Aungon Nag Radon

Ivan Kelly

Sumeet Rohatgi

Hao Zhan

Aditya Kamble

Tyler Bakke

Rahul Merulinath

Ryan Flynn

Systems and methods are described for customizable data streams in a streaming data processing system. Routing criteria for the customizable data streams are defined by a user, an automated process, or any other process. The routing criteria can be defined using graphical controls. The streaming data processing system uses the routing criteria to determine data that should be used to populate a particular data stream. Further, processing pipelines are customized such that a particular processing pipeline can obtain data from a particular user defined data stream and write data to a particular user defined data stream. Data is routed through the user defined data streams and customized processing pipelines based on a data route. A data route for a set of data may include multiple user defined data streams and multiple processing pipelines. The data route can include a loop of processing pipelines and data streams.

User interface for customizing data streams and processing pipelines

Various implementations of the present application set forth a computer-implemented method comprising obtaining, by a low-power hub device, a first set of data published by an edge device, where the low-power hub device subscribes to at least a subset of data published by the edge device, generating, by the low-power hub device, a second set of data from the first set of data by inputting the first set of data into a machine learning (ML) model executing on the low-power hub device, and transmitting the second set of data to a remote server computer system.

Real-time machine learning at an edge of a distributed network

A process for facilitating autoscaling in a stateful system is described herein. In embodiments, a set of metrics associated with a set of components of a stateful service is obtained. The set of metrics may generally indicate a utilization or a load of each of the components of the set of components (e.g., message managers and/or data stores). Thereafter, it is determined to initiate a scaling event at the stateful service in association with the set of components of the stateful service based on at least a portion of the set of metrics attaining a metric threshold indicating a threshold value for determining whether to scale stateful service components. A scaling request can then be provided to the stateful service to initiate the scaling event at the stateful service in association with the set of components of the stateful service.

Autoscaling in a stateful system

User defined data stream for routing data to a data destination based on a data route

User interface for customizing data streams

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Sunnyvale, CA, United States of America

Karthikeyan Ramasamy