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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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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

Netflix applies data science to hundreds of use cases across the company, including optimizing content delivery and video encoding. Data scientists at Netflix relish our culture that empowers them to work autonomously and use their judgment to solve problems independently. How could we improve the quality of life for data scientists?

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Architected for resiliency: How Dynatrace withstands data center outages

Dynatrace

And the last sentence of the email was what made me want to share this story publicly, as it’s a testimonial to how modern software engineering and operations should make you feel. In our case that includes the login to our SaaS tenants and exploring captured data. Let me start with the end-user impact.

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. Curious to learn more about other Data Science and Engineering functions at Netflix?

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Best PostgreSQL GUI [2024]

Scalegrid

Structured Query Language (SQL) is a simple declarative programming language utilized by various technology and business professionals to extract and transform data. Visualization & Reporting: Can the tool generate reports or visual representations like ER diagrams, data charts, or query execution plans?

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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?

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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

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