Splunk Inc.

Sakib Mehasanewala

Omprakaash Thoppai

Tristan Antonio Fletcher

Subramaniam Baskaran

Marc Andre Chene

Yogesh Sontakke

Systems and methods ingest machine data including logs, metadata, and cost and usage information from multiple heterogeneous cloud services. The machine data is saved as events. An application retrieves the metadata, events, metrics, and logs and causes an easy to understand visual representation of costs, resource usage, and non-compliance for each of a client's cloud services. Further, the data across the client's multiple heterogeneous cloud services is normalized to provide visual representations that compare the costs, resource usage, and non-compliance across the client's multiple heterogeneous cloud services. Further, machine learning aspects of the application can provide recommendations and trend analysis for cloud service asset usage.

Networked cloud service monitoring

Techniques are described for providing a cloud data collector (CDC) application for managing the generation of infrastructure templates. The CDC application provides graphical user interfaces that enable a user to provide inputs indicating configurations of data to be ingested by the data intake and query system, each configuration including one or more user accounts, in addition to data sources and regions associated with data sources. Using the configurations provided as input to the CDC application, the CDC application generates an infrastructure template that can be used to configure the service provider network to provide the requested security data to the data intake and query system.

Generating infrastructure templates for facilitating the transmission of user data into a data intake and query system

Information technology networked cloud service monitoring

Workflows for configuring the ingestion of user data from a service provider network into a data intake and query system

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Fremont, CA, United States of America

Sakib Mehasanewala