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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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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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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.

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Software engineering for machine learning: a case study

The Morning Paper

Software engineering for machine learning: a case study Amershi et al., More specifically, we’ll be looking at the results of an internal study with over 500 participants designed to figure out how product development and software engineering is changing at Microsoft with the rise of AI and ML. ICSE’19.

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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI data analysis can help development teams release software faster and at higher quality. So how can organizations ensure data quality, reliability, and freshness for AI-driven answers and insights? And how can they take advantage of AI without incurring skyrocketing costs to store, manage, and query data?