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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.

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

The Netflix TechBlog

As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. How can we develop templated detection modules (rules- and ML-based) and data streams to increases speed of development?

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Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards?—?always Others have grown into new areas as part of their professional development at Netflix.

Analytics 243
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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.

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Scaling Appsec at Netflix (Part 2)

The Netflix TechBlog

Our goal is to manage security risks to Netflix via clear, opinionated security guidance, and by providing risk context to Netflix engineering teams to make pragmatic risk decisions at scale. including bug bounty, pentesting, PSIRT (product security incident response), security reviews, and developer security education?—?via

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training. Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance. Internally, we use a production workflow orchestrator called Maestro.

Systems 235
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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Another dimension of scalability to consider is the size of the workflow.

Java 211