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| <link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">3 posts tagged with "uber" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="3 posts tagged with "uber" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/uber"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="description" content="Apache Pinot"><meta data-react-helmet="true" name="description" content="Real-time Analytics"><meta data-react-helmet="true" name="description" content="User-facing Analytics"><meta data-react-helmet="true" name="description" content="user facing analytics"><meta data-react-helmet="true" name="description" content="Olap"><meta data-react-helmet="true" name="description" content="Consumer-facing facing analytics"><meta data-react-helmet="true" name="description" content="Fast Analytics"><meta data-react-helmet="true" name="description" content="Who uses Apache Pinot?"><meta data-react-helmet="true" name="keywords" content="Pinot,Pinot SQL,Pinot Presto,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/uber"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/uber" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/uber" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f8c6b66d.css"> |
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Charon</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li><li class="sidebarItem_2UVv"><a class="sidebarItemLink_1RT6" href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>3 posts tagged with "uber"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_GeHD"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_291c margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_GeHD"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_291c margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_GeHD"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_291c margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener noreferrer" 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