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Legacy Architecture Modernisation With Strategic Domain-Driven Design

Strategic Tech

Most organisations go through an architecture modernisation effort at some point as their systems drift into a state of intolerable maintenance costs and they diverge too far from modern technological advances. What architecture will be optimal for enabling that business vision? How are we going to deliver the new architecture?

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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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Architecture Ownership Patterns for Team Topologies. Part 3: Multi-Team Patterns

Strategic Tech

Shared identity services and product branding providing a more consistent experience, and shared infrastructure can enable greater productivity. Individual teams may be full-stack/end-to-end or dedicated presentation/domain teams. An Experience Group is a collection of teams which handle only experience and presentation concerns.

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Sequencing Architecture Modernization: Risk Averse vs Risk Tolerant

Strategic Tech

Modernizing an architecture usually take years. This presents technology leaders with one of the most difficult modernization challenges: where to start and in which order to modernize? In the Architecture Modernization Sequencing Grid, this is the bottom right corner. Starting with something brand new defers these discoveries.

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How architecture evolves into strategy

O'Reilly Software

I should start by saying this section does not offer a treatise on how to do architecture. Vitruvius and the principles of architecture. Architecture begins when someone has a nontrivial problem to be solved. Everyone who goes to architecture school learns his work. It must be beautiful, like Venus, inspiring love.

Strategy 100
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

About two years ago, we, at our newly formed Machine Learning Infrastructure team started asking our data scientists a question: “What is the hardest thing for you as a data scientist at Netflix?” Our job as a Machine Learning Infrastructure team would therefore not be mainly about enabling new technical feats.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. What: The Modern Stack of ML Infrastructure. Adapted from the book Effective Data Science Infrastructure. Foundational Infrastructure Layers.

DevOps 137