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Dynatrace Announces Performance Monitoring for AWS Lambda Functions and Voice-Based Interactions with Amazon Alexa

Dynatrace announced code-level monitoring for serverless AWS Lambda functions.

With the AWS Lambda serverless compute service becoming increasingly popular for IoT and Alexa skills,

Dynatrace is also announcing that organizations can monitor performance across the whole omnichannel landscape, covering web, mobile and now voice-based interactions with Amazon Alexa.

“Amazon Alexa is becoming a key part of omnichannel experiences. Today, people buy and transact business via voice, not just on their laptop or mobile device. Monitoring these transactions at a user level is critical, as without it, you have an increasingly big blind-spot,” said Alois Reitbauer, VP, Chief Technical Strategist and Head of Innovation Lab at Dynatrace. “It’s similar with IoT; you now have millions of devices that are driving the code that’s being executed in AWS Lambda, so organizations need automated real-time discovery and continuous visibility to deliver proactive performance management and avoid IoT failures.”

Organizations are using AWS Lambda for event-driven applications such as Alexa skills and IoT deployments to eliminate server costs, improve availability and move to a self-managing infrastructure model. Now with Dynatrace, organizations using AWS Lambda can benefit from deep visibility into how code is running in AWS Lambda functions and as a result understand its impact on overall application performance and user experience.

“AWS Lambda represents true innovation in the cloud and an evolution in the abstraction of hardware. However, in these highly dynamic environments where code doesn’t sit on a server, but on compute power that appears and disappears in milliseconds, monitoring and managing performance in the traditional way is impossible,” Reitbauer continued. “With our AI-driven approach to performance monitoring and management, organizations can now shine a light on AWS Lambda functions to optimize the digital user experience.”

AWS Lambda makes it easy to execute code in response to events, such as changes to Amazon S3 buckets, updates to an Amazon DynamoDB table, or custom events generated by applications or devices. Dynatrace automatically instruments that execution environment in real-time, without the need for any code-level changes, enabling continuous, end-to-end full stack visibility.

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Dynatrace Announces Performance Monitoring for AWS Lambda Functions and Voice-Based Interactions with Amazon Alexa

Dynatrace announced code-level monitoring for serverless AWS Lambda functions.

With the AWS Lambda serverless compute service becoming increasingly popular for IoT and Alexa skills,

Dynatrace is also announcing that organizations can monitor performance across the whole omnichannel landscape, covering web, mobile and now voice-based interactions with Amazon Alexa.

“Amazon Alexa is becoming a key part of omnichannel experiences. Today, people buy and transact business via voice, not just on their laptop or mobile device. Monitoring these transactions at a user level is critical, as without it, you have an increasingly big blind-spot,” said Alois Reitbauer, VP, Chief Technical Strategist and Head of Innovation Lab at Dynatrace. “It’s similar with IoT; you now have millions of devices that are driving the code that’s being executed in AWS Lambda, so organizations need automated real-time discovery and continuous visibility to deliver proactive performance management and avoid IoT failures.”

Organizations are using AWS Lambda for event-driven applications such as Alexa skills and IoT deployments to eliminate server costs, improve availability and move to a self-managing infrastructure model. Now with Dynatrace, organizations using AWS Lambda can benefit from deep visibility into how code is running in AWS Lambda functions and as a result understand its impact on overall application performance and user experience.

“AWS Lambda represents true innovation in the cloud and an evolution in the abstraction of hardware. However, in these highly dynamic environments where code doesn’t sit on a server, but on compute power that appears and disappears in milliseconds, monitoring and managing performance in the traditional way is impossible,” Reitbauer continued. “With our AI-driven approach to performance monitoring and management, organizations can now shine a light on AWS Lambda functions to optimize the digital user experience.”

AWS Lambda makes it easy to execute code in response to events, such as changes to Amazon S3 buckets, updates to an Amazon DynamoDB table, or custom events generated by applications or devices. Dynatrace automatically instruments that execution environment in real-time, without the need for any code-level changes, enabling continuous, end-to-end full stack visibility.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...