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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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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. For many people, this is a waste of time; it’s pretentious developers geeking out over unnecessary perfectionism.

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

Dynatrace

By 2023, over 500 million digital apps and services will be developed and deployed using cloud-native approaches. For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. Industry apps explosion. Performance-as-a-self-service .

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AI meets operations

O'Reilly

Source code is relatively less important compared to typical applications; the training data is what determines how the model behaves, and the training process is all about tweaking parameters in the application so that it delivers correct results most of the time. You need a repository for models and for the training data. That’s the basics.

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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 does a modern technology stack for streamlined ML processes look like? All ML projects are software projects. Why: Data Makes It Different.

DevOps 138
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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. We heard many stories about difficulties related to data access and basic data processing. While a typical machine learning workflow running on Metaflow touches only a small shard of this warehouse, it can still process terabytes of data.

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From Domains to Value Streams

Strategic Tech

The 2010s were a turning-point in the history of software engineering. or “How do software architecture, domains, Conway’s Law, Team Topologies, and value streams all fit together?”. Team Flow Event Storming is also a great technique for mapping out and visualizing value stream-like processes collaboratively.