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1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. It also becomes inefficient as the data scale increases.

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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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How TripleLift Built an Adtech Data Pipeline Processing Billions of Events Per Day

High Scalability

This is a guest post by Eunice Do , Data Engineer at TripleLift , a technology company leading the next generation of programmatic advertising. The system is the data pipeline at TripleLift. TripleLift is an adtech company, and like most companies in this industry, we deal with high volumes of data on a daily basis.

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2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

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SIEM Volume Spike Alerts Using ML

DZone

SIEM stands for Security Information and Event Management. Key Components in SIEM Log Collection: SEIM systems collect and aggregate log data from Various sources across an organization’s network, including servers, endpoints, firewalls, applications, and other devices. This helps in detecting threats and attacks in real time.

Storage 136
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3. Psyberg: Automated end to end catch up

The Netflix TechBlog

Input : List of source tables and required processing mode Output : Psyberg identifies new events that have occurred since the last high watermark (HWM) and records them in the session metadata table. Data Load Type : The ETL can either load the missed/new data specifically or reload the entire specified range.

Tuning 244
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Explainer flow is event-triggered by an upstream flow, such Model A, B, C flows in the illustration. 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.

Systems 226