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

Netflix Tech

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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Ensuring the Successful Launch of Ads on Netflix

Netflix Tech

In this blog post, we’ll discuss the methods we used to ensure a successful launch, including: How we tested the system Netflix technologies involved Best practices we developed Realistic Test Traffic Netflix traffic ebbs and flows throughout the day in a sinusoidal pattern. Basic with ads was launched worldwide on November 3rd.

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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving , training , CI/CD, feature serving , and model monitoring systems. However, streaming data was not supported as a first-class citizen across many of the platform’s systems — such as training, complex monitoring, and others.

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Safe Agile Ceremonies: Your Ultimate Guide

Knowledge Hut

” ― Denis Waitley What are SAFe Agile Events (or) Ceremonies? The key link between these levels is the SAFe-specific events which help with smooth value delivery facilitation. The events help with alignment across teams, ARTs, etc thus helping in managing risk by providing a level-based cadence and synchronization.

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Leveraging Flink to Detect User Sessions and Engage DoorDash Consumers with Real-Time Notifications

DoorDash Engineering

To do this, we built a large stateful Flink job to track user sessions using mobile/web client events. Deciding whether to trigger in the frontend or backend Backend events typically are generated by internal services, while frontend events are created by the mobile app or web. Figure 2: Cart abandonment V1 workflow.

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Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. It also allowed us to optimize for handling time-series data and event data at scale. Overall, these systems allow us to make timely decisions for the business with accurate predictions.

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Lessons from debugging a tricky direct memory leak

Pinterest Engineering

Sanchay Javeria | Software Engineer, Ads Data Infrastructure To support metrics reporting for ads from external advertisers and real-time ad budget calculations at Pinterest, we run streaming pipelines using Apache Flink. Figure 2: Back pressure on various operators in the application, red markers signal back pressure at a given time.