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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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Data Engineers of Netflix?—?Interview with Samuel Setegne

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. What drew you to Netflix?

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What is IT automation?

Dynatrace

As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. This requires significant data engineering efforts, as well as work to build machine-learning models.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Without these integrations, projects would be stuck at the prototyping stage, or they would have to be maintained as outliers outside the systems maintained by our engineering teams, incurring unsustainable operational overhead. Importantly, all the use cases were engineered by practitioners themselves.

Systems 226
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How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. In the Efficiency space, our data teams focus on transparency and optimization.

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A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

The Netflix TechBlog

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207