Remove Metrics Remove Scalability Remove Software Engineering
article thumbnail

What is platform engineering?

Dynatrace

With growing multicloud complexity and the need for organization-wide scalability, self-service and automation capabilities have become increasingly essential for developer productivity. In response to this shift, platform engineering is growing in popularity. The result is a cloud-native approach to software delivery.

article thumbnail

Up your quality and agility factor – using automation to build “performance-as-a-self-service”

Dynatrace

For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. One way to apply improvements is transforming the way application performance engineering and testing is done. Here is a shortlist to get you started.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Unlock the Power of DevSecOps with Newly Released Kubernetes Experience for Platform Engineering

Dynatrace

Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026. Open source logs and metrics take precedence in the monitoring process.

article thumbnail

Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. Recovery time of the throughput metric.

article thumbnail

AWS observability: AWS monitoring best practices for resiliency

Dynatrace

Like general observability , AWS observability is the capacity to measure the current state of your AWS environment based on the data it generates, including its logs, metrics, and traces. While this provides greater scalability than on-site instrumentation, it also introduces complexity. And why it matters.

article thumbnail

Conducting log analysis with an observability platform and full data context

Dynatrace

Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams. Dynatrace Grail unifies data from logs, metrics, traces, and events within a real-time model.

Analytics 246
article thumbnail

Dynatrace extends distributed tracing for serverless on AWS Lambda (GA)?

Dynatrace

A single pane of glass to view trace information along with AWS CloudWatch metrics. See your AWS serverless workloads in full context with customer experience and business outcome metrics. – Robert Trueman, Head of Software Engineering at CDL. trace information along with AWS CloudWatch metrics. at all times?

Lambda 264