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Improve Efficiency With Smaller Code Reviews

DZone

In the ever-evolving software paradigm, oftentimes multiple developers work on the shared code base collaboratively. Code management becomes challenging with the number of developers, the scope of change, the pace of delivery, etc on a shared code base.

Code 278
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The Relationship Between Software Architecture And Business Models (and more)

Strategic Tech

If every significant architecture decision has business consequences, then knowing the business model and which trade-offs to choose is maybe the most important skill of architects. But what is the actual relationship between a business model and a software architecture? A software system is a model of a domain.

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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. However, we noticed that GPT 3.5

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Microservices vs. monolithic architecture: Understanding the difference

Dynatrace

As the pace of business quickens, software development has adapted. Increasingly, teams release software features more quickly to accommodate customer needs. As a result, organizations are weighing microservices vs. monolithic architecture to improve software delivery speed and quality. Easier to develop.

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ABAC on SpiceDB: Enabling Netflix’s Complex Identity Types

The Netflix TechBlog

Netflix is always looking for security, ergonomic, or efficiency improvements, and this extends to authorization tools. Once you accept that adding input arguments to the distributed cache isn’t efficient, you naturally gravitate toward the first question: what if you keep those inputs out of the cached subproblems?

Cache 249
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Building High-Quality Software

DZone

NIST did classic research to show that catching bugs at the beginning of the development process could be more than ten times cheaper than if a bug reaches production. "}">I have interviewed many engineers and managers lately, and one of the standard questions I ask is how to build high-quality software.

Software 245
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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. To make ML applications production-ready from the beginning, developers must adhere to the same set of standards as all other production-grade software.

DevOps 137