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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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Scaling Appsec at Netflix (Part 2)

The Netflix TechBlog

By Astha Singhal , Lakshmi Sudheer , Julia Knecht The Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms.

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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Curious to learn about what it’s like to be a Data Engineer at Netflix? Hear directly from Samuel Setegne , Dhevi Rajendran , Kevin Wylie , and Pallavi Phadnis in our “Data Engineers of Netflix” interview series. Say we are developing a new way to generate a piece of evidence, such as a trailer.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance. By targeting @titus, Metaflow tasks benefit from these battle-hardened features out of the box, with no in-depth technical knowledge or engineering required from the ML engineers or data scientist end.

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What is IT automation?

Dynatrace

Developing automation takes time. This requires significant data engineering efforts, as well as work to build machine-learning models. The Dynatrace Software Intelligence Platform provides automatic and intelligent observability that overcomes the challenges of automating IT processes for cloud-native environments.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

These challenges are currently addressed in suboptimal and less cost efficient ways by individual local teams to fulfill the needs, such as Lookback: This is a generic and simple approach that data engineers use to solve the data accuracy problem. Users configure the workflow to read the data in a window (e.g.