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In this blog post, we discuss the scale at which Twitter operates through Project Sparrow, an initiative that shifted the architecture of our data pipelines from a batch event approach to streaming.
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Over-squashing is a common plight of Graph Neural Networks. In this post, we discuss how this phenomenon can be understood and remedied through the concept of Ricci curvature.
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In this blog, we share findings from a follow-up analysis in which we seek to understand how prompts cause people to reconsider potentially harmful or offensive content before they hit send.
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In this blog, we discuss how we approached migrating an exabyte of data to Google Cloud to make it easier for our Tweeps to analyze and visualize data.
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In this blog, we share an overview of Twitter’s Druid ecosystem and discuss our work towards a unified ingestion experience.
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On the Data Science team, a piece of our job is understanding how conversations are happening on Twitter. In this blog, we use Wordle as a case study to showcase how we think about similar analyses.
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This blog post discusses how feature propagation can be an efficient and scalable approach for handling missing features in graph machine learning applications.
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In this blog post, we talk about a performance and stability problem we encountered while migrating Manhattan's storage engine to RocksDB and how we solved it.
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In this blog, we share details about how we built an in-house product called Qurious, which allows our internal customers to get answers to their analytical queries through natural language questions.
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Twitter’s new Thrift library is an open-source, standalone, lightweight, data encoding library. In this blog, we share our library so iOS developers outside Twitter can start using Thrift data.
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