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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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Legacy Architecture Modernisation With Strategic Domain-Driven Design

Strategic Tech

Before jumping into either of those scenarios, have a look at what Strategic Domain-Driven Design can offer you. It’s got a selection of free tools you can use for defining your technology strategy, shaping your architectural boundaries, and organising your teams. How are we going to deliver the new architecture?

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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. Both valuing design and striving for continuous delivery are necessary. So we need to make it part of everything we do.

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How architecture evolves into strategy

O'Reilly Software

It's a given that we must design a system, including a local software architecture, that actually runs, that is "solid." This is supported in real terms through standards and consistent application of conventions, both in the information architecture (i.e., It must be useful, have utility. Solid doesn't mean inflexible.

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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Improving The Performance Of Wix Websites (Case Study)

Smashing Magazine

Implementing this change enabled us to take major steps such as updating our infrastructure along with completely rewriting our core functionality from the ground up. This high rate of growth, coupled with the current scale and diversity of offerings presents a huge challenge when setting out to improve performance.

Website 126
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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. In effect, the engineer designs and builds the world wherein the software operates. What: The Modern Stack of ML Infrastructure.

DevOps 138