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Sustainability: Thoughts from a software engineer

Dynatrace

Until recently, improvements in data center power efficiency compensated almost entirely for the increasing demand for computing resources. The rise of big data, cryptocurrencies, and AI means the IT sector contributes significantly to global greenhouse gas emissions. However, this trend is now reversing.

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Bringing Software Engineering Rigor to Data

DZone

In software engineering, we've learned that building robust and stable applications has a direct correlation with overall organization performance. The data community is striving to incorporate the core concepts of engineering rigor found in software communities but still has further to go. Posted with permission.

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A Five-Step Methodology for Maximizing Efficiency in Software Engineering Meetings

DZone

Meetings are a crucial aspect of software engineering , serving as a collaboration, communication, and decision-making platform. However, they often come with challenges that can significantly impact the efficiency and productivity of software development teams.

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The Next Major Shift in Enterprise Software Engineering: From Platforms to “Platformless”

DZone

The evolution of enterprise software engineering has been marked by a series of "less" shifts — from client-server to web and mobile ("client-less"), data center to cloud ("data-center-less"), and app server to serverless.

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Low-Maintenance Backend Architectures for Scalable Applications

DZone

After years of working in the intricate world of software engineering, I learned that the most beautiful solutions are often those unseen: backends that hum along, scaling with grace and requiring very little attention. Developers could understand and manage the entire systems intricacies.

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Our First Netflix Data Engineering Summit

The Netflix TechBlog

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. Failures can occur unpredictably across various levels, from physical infrastructure to software layers. Recovery time of the latency p90.