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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Since then, open-source Metaflow has gained support for Argo Workflows , a Kubernetes-native orchestrator, as well as support for Airflow which is still widely used by data engineering teams. In other cases, it is more convenient to share the results via a low-latency API.

Systems 226
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

4:45pm-5:45pm NFX 209 File system as a service at Netflix Kishore Kasi , Senior Software Engineer Abstract : As Netflix grows in original content creation, its need for storage is also increasing at a rapid pace. Technology advancements in content creation and consumption have also increased its data footprint. Wednesday?—?December

AWS 100
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

4:45pm-5:45pm NFX 209 File system as a service at Netflix Kishore Kasi , Senior Software Engineer Abstract : As Netflix grows in original content creation, its need for storage is also increasing at a rapid pace. Technology advancements in content creation and consumption have also increased its data footprint. Wednesday?—?December

AWS 100
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Friends don't let friends build data pipelines

Abhishek Tiwari

Lastly, we will talk about the internal platform and product divide – one key reason why data pipeline initiatives typically fail – and why it is better working backward from the product. Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. A data pipeline is a software which runs on hardware.

Latency 63
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

4:45pm-5:45pm NFX 209 File system as a service at Netflix Kishore Kasi , Senior Software Engineer Abstract : As Netflix grows in original content creation, its need for storage is also increasing at a rapid pace. Technology advancements in content creation and consumption have also increased its data footprint. Wednesday?—?December

AWS 37
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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful).