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Overview for netflix

Best Practices for Managing Security Operations on AWS

Learn how Netflix utilizes a combination of several services such as CloudTrail, CloudWatch Events, and the AWS service APIs to operationalize monitoring of their deployments at scale. You willl also learn the changes made as Netflix’s deployment has grown over the years. 

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Apache Spark-Based Stratification Library for Machine Learning Use Cases at Netflix

Building flexible machine learning libraries adapted for Netflix’s use cases is paramount in continued efforts to better model the users’ behaviors and provide them great personalized video recommendations.

This talk introduces one such spark-based stratification library developed at Netflix to aid “Training Set Stratification” in offline machine learning workflows. Originally created to implement user selection algorithms in data snapshotting infrastructure, the library has evolved to cater to general-purpose stratification use cases in ML pipelines.

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Fact Store at Scale for Netflix Recommendations

Learn how Netflix uses Spark and Scala extensively and variety of compression techniques to store and retrieve data efficiently.

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Near Real-Time Netflix Recommendations Using Apache Spark Streaming

Nitin Sharma and Elliot Chow software engineers at Netflix  take a deep dive into realtime Spark Streaming ecosystem at Netflix. Both it’s infrastructure and business use cases. On the infrastructure front, they delve into scale challenges, state management, data persistence, resiliency considerations, metrics, operations and auto-remediation.

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Stream Processing with Flink at Netflix

Netflix's stream processing system processes over 4 trillion events per day (36GB/sec) using thousands of servers. To enable teams within the organization, they've built a self-service infrastructure that is built on Apache Flink and Apache Kafka. Much of this presentation centralizes around why and how they use Flink, including what scalability challenges they've faced (e.g. in checkpointing), fine grained recovery, and how they implement reprocessing from scratch.

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