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The project aim was to address Azure services' needs for fast and scalable log and telemetry analytics. In 2016 it became the backend big-data and analytics service for Application Insights Analytics.
Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. [2]
Kyvos is a business intelligence acceleration platform for cloud and big data platforms developed by an American privately held company named Kyvos Insights.The company, headquartered in Los Gatos, California, was founded by Praveen Kankariya, CEO of Impetus Technologies.
Dynatrace, Inc. is a global technology company that provides a software observability platform based on artificial intelligence (AI) and automation.Dynatrace technologies are used to monitor, analyze, and optimize application performance, software development and security practices, IT infrastructure, and user experience for businesses and government agencies throughout the world.
Arm MAP, a performance profiler supporting Linux platforms.; AppDynamics, an application performance management solution [buzzword] for C/C++ applications via SDK.; AQtime Pro, a performance profiler and memory allocation debugger that can be integrated into Microsoft Visual Studio, and Embarcadero RAD Studio, or can run as a stand-alone application.
azure.microsoft.com /en-us /services /stream-analytics / Microsoft Azure Stream Analytics is a serverless scalable complex event processing engine by Microsoft that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, web sites, social media, and other applications. [ 1 ]
LoadRunner is a software testing tool from OpenText.It is used to test applications, measuring system behavior and performance under load.. LoadRunner can simulate millions of users concurrently using application software, recording and later analyzing the performance of key components of the application whilst under load.
Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage, [9] AWS Batch for offline batch processing, [9] [10] or Amazon Kinesis for real-time processing.