Remove Data Engineering Remove Efficiency Remove Latency
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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 165
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Pushy to the Limit: Evolving Netflix’s WebSocket proxy for the future

The Netflix TechBlog

With these clear benefits, we continued to build out this functionality for more devices, enabling the same efficiency wins. It was very efficient, but it had a set job size, requiring manual intervention if we wanted to horizontally scale it, and it required manual intervention when rolling out a new version.

Latency 230
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Optimizing data warehouse storage

The Netflix TechBlog

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 208
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These 7 Edge Data Challenges Will Test Companies the Most in 2025

VoltDB

Edge computing has transformed how businesses and industries process and manage data. By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. As data streams grow in complexity, processing efficiency can decline.

IoT 52
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. Users configure the workflow to read the data in a window (e.g.

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Netflix at AWS re:Invent 2019

The Netflix TechBlog

This talk explores the journey, learnings, and improvements to performance analysis, efficiency, reliability, and security. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. In 2019, Netflix moved thousands of container hosts to bare metal.

AWS 38
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. the retry success probability) and compute cost efficiency (i.e., Multi-objective optimizations.

Tuning 213