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Seamless AI-powered observability for multicloud serverless applications

Dynatrace

Cloud vendors such as Amazon Web Services (AWS), Microsoft, and Google provide a wide spectrum of serverless services for compute and event-driven workloads, databases, storage, messaging, and other purposes. AI-powered automation and deep, broad observability for serverless architectures. Dynatrace news.

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What is Google Cloud Functions?

Dynatrace

In recent years, function-as-a-service (FaaS) platforms such as Google Cloud Functions (GCF) have gained popularity as an easy way to run code in a highly available, fault-tolerant serverless environment. What is Google Cloud Functions? GCF is part of the Google Cloud Platform. How Google Cloud Functions works.

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

The phrase “serverless computing” appears contradictory at first, but for years now, successful companies have understood the benefit of using serverless technologies to streamline operations and reduce costs. So what exactly does “serverless” mean, and how can your organization benefit from it?

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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

As companies accelerate digital transformation, they implement modern cloud technologies like serverless functions. According to Flexera , serverless functions are the number one technology evaluated by enterprises and one of the top five cloud technologies in use at enterprises. What are serverless applications?

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What is function as a service? App development gets FaaS and furious

Dynatrace

FaaS enables developers to create and run a single function in the cloud using a serverless compute model. Cloud providers such as Google, Amazon Web Services, and Microsoft also followed suit with frameworks such as Google Cloud Functions , AWS Lambda , and Microsoft Azure Functions. How does function as a service work?

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Breaking data silos: Liquid Reply’s journey to custom API observability with OpenTelemetry and Dynatrace

Dynatrace

Data is proliferating in separate silos from containers and Kubernetes to open source APIs and software to serverless compute services, such as AWS and Azure. The organization needed to ensure the correlation of all events in a complete end-to-end trace. However, they had numerous custom applications with separate APIs.

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Implementing AWS well-architected pillars with automated workflows

Dynatrace

And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks? For example, optimizing resource utilization for greater scale and lower cost and driving insights to increase adoption of cloud-native serverless services.

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