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I asked ChatGPT to write a blog post about Data Engineering. Here it is.

Confessions of a Data Guy

Data engineering is a vital field within the realm of data science that focuses on the practical aspects of collecting, storing, and processing large amounts of data. Here it is. appeared first on Confessions of a Data Guy.

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Rebuilding Netflix Video Processing Pipeline with Microservices

Netflix Tech

This introductory blog focuses on an overview of our journey. Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process.

Process 93
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Rapid Event Notification System at Netflix

Netflix Tech

To this end, we developed a Rapid Event Notification System (RENO) to support use cases that require server initiated communication with devices in a scalable and extensible manner. In this blog post, we will give an overview of the Rapid Event Notification System at Netflix and share some of the learnings we gained along the way.

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms.

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Feature Caching for Recommender Systems w/ Cachelib

Pinterest Engineering

Manager, Engineering | At Pinterest, we operate a large-scale online machine learning inference system, where feature caching plays a critical role to achieve optimal efficiency. Background Recommender systems are fundamental to Pinterest’s mission to inspire users to create a life they love.

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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

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Last Mile Data Processing with Ray

Pinterest Engineering

Behind the scenes, hundreds of ML engineers iteratively improve a wide range of recommendation engines that power Pinterest, processing petabytes of data and training thousands of models using hundreds of GPUs. It often requires a long process that touches many languages and frameworks. As model architecture building blocks (e.g.