Cisco Technology, Inc.

Fortinet, Inc.

Lacework Inc.

Ashwin Jayaprakash

Yijou Chen

Venkata Rama Rajesh Gudivada

Vinay Srinivasaiah

Sowmya A Karmali

Jaideep Padhye

Puneet Anand

Jean-philippe E Martin

Julien Sobrier

Ryan Nicholas TerBush

Tom Thekkel Jose

Sai Samrat Karlapudi

Ross T Bunker

Gurunatha Karaje

Anastasios Arvanitis

Juhwan Jeong

John Solomon Jebamani Asirvatham

Anna E Min

Ankita Bhargava

Helgi Sigurbjarnarson

Ryan W Nicholson

Amol Patil

Carter T Abdallah

An example method includes: receiving, by a data platform configured to collect data from a plurality of service endpoints of a cloud environment, a request to onboard an additional service endpoint for data collection by the data platform; collecting, by the data platform operating in a preview mode of operation based on the request, first data from the plurality of service endpoints and second data from the additional service endpoint; monitoring, by the data platform operating in the preview mode of operation based on the request, for errors associated with the collecting of the first data and the second data; disabling, by the data platform when the monitoring detects an error within a defined period, data collection from the additional service endpoint; and integrating, by the data platform when the monitoring does not detect an error within the defined period, the additional service endpoint into normal data collection.

Expanding data collection from a monitored cloud environment

Activity monitoring of a cloud compute environment based on container orchestration data may be performed by a data platform. For example, the data platform may obtain audit log data generated by a container orchestrator within a cloud compute environment, generate a data model based on the audit log data, and use the data model to monitor the activity for a security issue. The data model may be indicative of activity occurring with respect to one or more containerized applications executing within the cloud compute environment and the monitored activity may be activity with respect to the one or more containerized applications. Corresponding methods, systems, and products are also disclosed.

Activity monitoring of a cloud compute environment based on container orchestration data

In one embodiment, a method for automatic application data collection is disclosed. The method illustratively comprises determining a stack trace associated with one or more business transactions, wherein at least one application is configured to perform at least part of the one or more business transactions; identifying, based on the stack trace, one or more methods of the at least one application that were invoked during performance of the one or more business transactions; selecting, from the identified one or more methods, a set of the one or more methods by applying at least one scoring heuristic to the identified one or methods; and configuring one or more monitoring agents executing on the at least one application to instrument the selected set of the one or methods at one or more instrumentation points, wherein application performance data is generated by instrumentation of the one or more instrumentation points.

Automatic application data collection for potentially insightful business values

In one embodiment, a device receives, via a user interface, definition of a first sequence of transactional milestones performed by users of an online application and identified using a first type of identifier. The device also receives, via the user interface, definition of a second sequence of transactional milestones performed by users of the online application and identified using a second type of identifier. The device further receives, via the user interface, definition of a key transition associated with at least one transactional milestone in the first sequence of transactional milestones or second sequence of transactional milestones that links the first type of identifier with the second type of identifier. The device represents, using the key transition, performance of the first sequence of transactional milestones and the second sequence of transactional milestones by a particular user of the online application as a unified sequence.

Key-based complex process monitoring

An example method includes accessing, by a data platform via a network, data from one or more cloud environments; identifying, by the data platform and in the data, first data associated with a first entity and a first data type and second data associated with a second entity and a second data type; mapping, by the data platform and based on the first entity and the first data type, the first data to a first data stream of a data streaming platform; mapping, by the data platform and based on the second entity and the second data type, the second data to a second data stream of the data streaming platform, the second data stream different from the first data stream of the data streaming platform; and generating, based on the first data stream, a graph representing activity associated with the first entity in the one or more cloud environments.

Universal cloud data ingestion for stream processing

Growing community of inventors

Sunnyvale, CA, United States of America

Ashwin Jayaprakash