Seamless AI-powered observability for multicloud serverless applications
With serverless systems, observability and job automation can be difficult. They are frequently made up of hundreds of loosely connected services from various sources. As a result, DevOps and SRE teams may evaluate, troubleshoot, and optimize serverless programs in real-time. It enables scalable innovation.
Serverless computing and event-driven workloads, databases, storage, communications, and other tasks are available from cloud providers such as Amazon Web Services (AWS), Microsoft, and Google. To construct a single application, engineers frequently select best-of-breed services from various sources. However, getting end-to-end visibility and real-time insights into these highly distributed, complicated settings is increasingly complex. End-to-end traceability is becoming increasingly important as the number of related functions and other services grows.
AI-powered observability automation and deep, broad observability for serverless architectures
We offer deep and broad observability and sophisticated AIOps capabilities to cover the most critical serverless services. This support now includes managed Kubernetes environments, message queues, and cloud databases across all major cloud providers and AWS Lambda.
Your DevOps teams will be able to see a complete picture of their multi-cloud serverless apps.
Extensible and straightforward approaches, such as PurePath® distributed tracing for end-to-end, automatic, code-level visibility, and Davis for root-cause investigation, make tracing simple. In serverless applications, this provides proactive AI-driven analysis and simple troubleshooting. re.
Davis also uses queues and other event systems to alert service-to-service communication issues automatically. As a result, DevOps teams can identify typical problem patterns in serverless services rather than event-driven architecture.
AI-powered observability
Easy and effortless FaaS insights with a single line of code
Open observability standards like OpenTelemetry are becoming increasingly crucial in overcoming instrumentation issues due to the limits of FaaS services in running third-party agents, such as constraints in executing third-party tools and limited access to the underlying infrastructure.
For initialization and basic instrumentation, OpenTelemetry often requires a significant boilerplate code. At the same time, cloud suppliers continue to engage in open standards (for example, AWS Distro for OpenTelemetry and Azure Monitor OpenTelemetry Exporter for.NET, Node.js, and Python applications), implementing them still requires a significant amount of effort.
The complete end-to-end visibility of the serverless tiers in your applications is enabled through enhanced tracing across your whole stack (see example below), which shows a trace with numerous serverless services, including functions, Cloud-Queues, serverless databases, and application services.
About Enteros
IT organizations routinely spend days and weeks troubleshooting production database performance issues across multitudes of critical business systems. Fast and reliable resolution of database performance problems by Enteros enables businesses to generate and save millions of direct revenue, minimize waste of employees’ productivity, reduce the number of licenses, servers, and cloud resources and maximize the productivity of the application, database, and IT operations teams.
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