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Part 3 - Understanding the Impact in Real Customer Scenarios</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit">Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot">How to Ingest Streaming Data from Kafka to Apache Pinot™</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work">Change Data Capture with Apache Pinot - How Does It Work?</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/05/18/apache-pinot-tutorial-for-getting-started-a-step-by-step-guide">Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance">StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot">Geospatial Indexing in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag">Apache Pinot™ 0.12 - Consumer Record Lag</a></li></ul></nav></aside><main class="col col--7"><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></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/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></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/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></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/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></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/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></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/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture, |
| but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></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/linked-in">LinkedIn</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></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/linked-in">LinkedIn</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></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/linked-in">LinkedIn</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></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/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</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></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data |
| and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></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/linked-in">LinkedIn</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></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/blog/page/3"><div class="pagination-nav__label">« Newer Entries</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/blog/page/5"><div class="pagination-nav__label">Older Entries »</div></a></div></nav></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 Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a 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