"use strict";(self.webpackChunkwebsite=self.webpackChunkwebsite||[]).push([[6558],{4137:function(e,n,t){t.d(n,{Zo:function(){return p},kt:function(){return m}});var i=t(7294);function r(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function a(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);n&&(i=i.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,i)}return t}function o(e){for(var n=1;n<arguments.length;n++){var t=null!=arguments[n]?arguments[n]:{};n%2?a(Object(t),!0).forEach((function(n){r(e,n,t[n])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(t)):a(Object(t)).forEach((function(n){Object.defineProperty(e,n,Object.getOwnPropertyDescriptor(t,n))}))}return e}function l(e,n){if(null==e)return{};var t,i,r=function(e,n){if(null==e)return{};var t,i,r={},a=Object.keys(e);for(i=0;i<a.length;i++)t=a[i],n.indexOf(t)>=0||(r[t]=e[t]);return r}(e,n);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(i=0;i<a.length;i++)t=a[i],n.indexOf(t)>=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(r[t]=e[t])}return r}var c=i.createContext({}),s=function(e){var n=i.useContext(c),t=n;return e&&(t="function"==typeof e?e(n):o(o({},n),e)),t},p=function(e){var n=s(e.components);return i.createElement(c.Provider,{value:n},e.children)},u={inlineCode:"code",wrapper:function(e){var n=e.children;return i.createElement(i.Fragment,{},n)}},g=i.forwardRef((function(e,n){var t=e.components,r=e.mdxType,a=e.originalType,c=e.parentName,p=l(e,["components","mdxType","originalType","parentName"]),g=s(t),m=r,d=g["".concat(c,".").concat(m)]||g[m]||u[m]||a;return t?i.createElement(d,o(o({ref:n},p),{},{components:t})):i.createElement(d,o({ref:n},p))}));function m(e,n){var t=arguments,r=n&&n.mdxType;if("string"==typeof e||r){var a=t.length,o=new Array(a);o[0]=g;var l={};for(var c in n)hasOwnProperty.call(n,c)&&(l[c]=n[c]);l.originalType=e,l.mdxType="string"==typeof e?e:r,o[1]=l;for(var s=2;s<a;s++)o[s]=t[s];return i.createElement.apply(null,o)}return i.createElement.apply(null,t)}g.displayName="MDXCreateElement"},3503:function(e,n,t){t.r(n),t.d(n,{frontMatter:function(){return l},contentTitle:function(){return c},metadata:function(){return s},assets:function(){return p},toc:function(){return u},default:function(){return m}});var i=t(7462),r=t(3366),a=(t(7294),t(4137)),o=["components"],l={title:"Analyzing anomalies with ThirdEye",author:"LinkedIn",author_title:"LinkedIn Engineering Team",author_url:"https://engineering.linkedin.com/blog/topic/pinot",author_image_url:"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png",description:"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.",keywords:["Pinot","LinkedIn","Anomaly Detection","Anomaly Alerts","User-Facing Analytics","Real-time data platform"],tags:["Pinot","LinkedIn","real-time data platform","Realtime","Analytics","User-Facing Analytics"]},c=void 0,s={permalink:"/blog/2020/02/20/LinkedIn-Thirdeye",editUrl:"https://github.com/apache/pinot-site/edit/dev/website/blog/2020-02-20-LinkedIn-Thirdeye.md",source:"@site/blog/2020-02-20-LinkedIn-Thirdeye.md",title:"Analyzing anomalies with ThirdEye",description:"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.",date:"2020-02-20T00:00:00.000Z",formattedDate:"February 20, 2020",tags:[{label:"Pinot",permalink:"/blog/tags/pinot"},{label:"LinkedIn",permalink:"/blog/tags/linked-in"},{label:"real-time data platform",permalink:"/blog/tags/real-time-data-platform"},{label:"Realtime",permalink:"/blog/tags/realtime"},{label:"Analytics",permalink:"/blog/tags/analytics"},{label:"User-Facing Analytics",permalink:"/blog/tags/user-facing-analytics"}],readingTime:.175,truncated:!1,authors:[{name:"LinkedIn",title:"LinkedIn Engineering Team",url:"https://engineering.linkedin.com/blog/topic/pinot",imageURL:"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png"}],prevItem:{title:"Using Apache Pinot and Kafka to Analyze GitHub Events",permalink:"/blog/2020/04/10/DevBlog-AnalyzeGitEvents"},nextItem:{title:"Engineering SQL Support on Apache Pinot at Uber",permalink:"/blog/2020/01/15/Pinot-Presto-SQL"}},p={authorsImageUrls:[void 0]},u=[],g={toc:u};function m(e){var n=e.components,t=(0,r.Z)(e,o);return(0,a.kt)("wrapper",(0,i.Z)({},g,t,{components:n,mdxType:"MDXLayout"}),(0,a.kt)("p",null,"Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data\nand help our engineers understand why these anomalies happened through a dimension heatmap"),(0,a.kt)("p",null,(0,a.kt)("a",{parentName:"p",href:"https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye"},"Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye")),(0,a.kt)("p",null,(0,a.kt)("img",{parentName:"p",src:"https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png",alt:"Analyzing anomalies with ThirdEye"})))}m.isMDXComponent=!0}}]);