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The State of DevOps Automation assessment: How automated are you?

Dynatrace

In response to the scale and complexity of modern cloud-native technology, organizations are increasingly reliant on automation to properly manage their infrastructure and workflows. It addresses the extent to which an organization prioritizes automation efforts, including budgets, ROI models, standardized best practices, and more.

DevOps 191
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DevOps observability: A guide for DevOps and DevSecOps teams

Dynatrace

From site reliability engineering to service-level objectives and DevSecOps, these resources focus on how organizations are using these best practices to innovate at speed without sacrificing quality, reliability, or security. SRE applies software engineering principles to operations and infrastructure processes.

DevOps 203
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Scaling Appsec at Netflix (Part 2)

The Netflix TechBlog

This approach has also allowed us to build strong relationships with central engineering teams at Netflix (Data Platform, Developer Tools, Cloud Infrastructure, IAM Product Engineering) that will continue to serve as central points of leverage for security in the long term. However, it has not been all sunshine and rainbows.

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SRE vs DevOps: What you need to know

Dynatrace

SRE is the transformation of traditional operations practices by using software engineering and DevOps principles to improve the availability, performance, and scalability of releases by building resiliency into apps and infrastructure. Adopting these practices is a culture shift. SRE vs DevOps?

DevOps 191
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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

Another key theme at Dynatrace Perform 2024 is organizations’ growing adoption of platform engineering , which helps accelerate the delivery of software applications. Platform engineering improves developer productivity by providing self-service capabilities with automated infrastructure operations.

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Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML

The Morning Paper

Enterprises in every industry are developing strategies for digitally transforming their businesses at every level. Many of the software engineering discipline and controls need to be brought over into an ML context. Flock treats ML models as software artefacts derived from data.