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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.

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10 talks to look for at the 2018 O'Reilly Software Architecture Conference in London

O'Reilly Software

From chaos architecture to event streaming to leading teams, the O'Reilly Software Architecture Conference offers a unique depth and breadth of content. We received more than 200 abstracts for talks for the 2018 O'Reilly Software Architecture Conference in London—on both expected and surprising topics.

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Nurturing Design in Your Software Engineering Culture

Strategic Tech

There are a few qualities that differentiate average from high performing software engineering organisations. I believe that attitude towards the design of code and architecture is one of them. In Accelerate , Nicole Forsgren shows a link between well-designed, loosely-coupled architecture and more frequent software delivery.

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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.

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Forming an Architecture Modernization Enabling Team (AMET)

Strategic Tech

Architecture modernization initiatives are strategic efforts involving many teams, usually for many months or years. An AMET is an architecture Enabling Team that helps to coordinate and upskill all teams and stakeholders involved in a modernization initiative. They need a more loosely coupled architecture and empowered teams.

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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. The infrastructure should allow them to exercise their freedom as data scientists but it should provide enough guardrails and scaffolding, so they don’t have to worry about software architecture too much.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Software Architecture. This approach is not novel.

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