Apache Pinot User Stories

Learn from your peers and industry leaders how and why they moved to Apache Pinot
for their real-time analytics use cases

    User Facing Analytics

    External Article

    Why Apache Pinot is Worth the Hype? Bazaar’s Success Story

    Bazaar incorporated Apache Pinot for user facing analytics. Find out which features made them choose Pinot.

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    Blog

    Uber: Serving Real-Time App Crash Analytics While Saving $2M+ with Apache Pinot

    Uber migrated to Apache Pinot to monitor mobile app crash analytics and saved more than $2 million annually.

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    Video

    Apache Pinot vs Apache Druid & Real-Time Analytics (LinkedIn Profile Insights)

    StarTree CEO, Kishore Gopalakrishna, discusses Apache Pinot and performance comparisons with Apache Druid at Crunch Data Conference 2018 in Budapest.

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    External Article

    Building LinkedIn Talent Insights to democratize data-driven decision making

    Leveraging Apache Pinot to build LinkedIn Talent Insights

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    Video

    Building Real-Time Analytics Applications Using Apache Pinot

    This talk will present how Pinot has become the de-facto solution for serving analytic queries in milliseconds, ad-hoc reporting, monitoring & Anomaly Detection on multidimensional data.

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    External Article

    Text analytics on LinkedIn Talent Insights using Apache Pinot

    Implementing and integrating keyword search with Pinot on the LinkedIn Talent Insights application.

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    Video

    Building Latency Sensitive User Facing Analytics via Apache Pinot

    Chinmay Soman discusses how LinkedIn, Uber and other companies managed to have low latency for analytical database queries in spite of high throughput.

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    External Article

    Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches

    Using the Theta Sketches based set intersection cardinality estimation using Apache Pinot to solve the audience-reach estimation problem in production.

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    External Article

    Bridging batch and stream processing for the Recruiter usage statistics dashboard

    Applying LinkedIn's codebase Lambda architecture to build a near real-time dashboard that helps recruiters utilize their product better.

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    Anomaly Detection

    External Article

    Monitoring business performance data with ThirdEye smart alerts

    We can think of data as the raw resource that business decisions are based on, and ThirdEye smart alerts as the environment that allows leaders to act on generated information swiftly and accurately.

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    External Article

    Analyzing anomalies with ThirdEye

    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.

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    External Article

    Smart alerts in ThirdEye, LinkedIn’s real-time monitoring platform

    While monitoring is a crucial aspect of this system, setting up the right alerts is critical for receiving meaningful notifications.

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    External Article

    Introducing ThirdEye: LinkedIn’s Business-Wide Monitoring Platform

    ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.

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    Observability

    External Article

    Real-time analytics on network flow data with Apache Pinot

    InFlow and Apache Pinot at LinkedIn

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    Case Study

    Webex: Real-Time Observability Using Pinot and Grafana

    Learn how Cisco Webex found latencies that were 5× to 150× lower with Apache Pinot over Elasticsearch.

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    Blog

    Webex Meets Unprecedented Analytics Demand with Pinot

    Learn how Cisco Webex adopted to spiking demand during the Covid-19 pandemic with Apache Pinot™

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    Video

    Webex: Real-Time Observability & Analytics with Apache Pinot

    Cisco Engineers share why they picked Apache Pinot as their real time analytics layer as opposed to other solutions like Elasticsearch and Clickhouse.

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    Security

    External Article

    Real-time Security Insights: Apache Pinot at Confluera

    This blog post will talk a bit about the architectural needs that led us to select Pinot and how it helped us build our product’s required capabilities.

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    Video

    Apache Pinot Concepts: Data Security

    Apache Pinot comes with a number of essential security features out of the box which we'll highlight in this talk.

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    Dashboards / BI

    Video

    Apache Pinot and Superset: Forecasting and Visualization

    In this lightning talk, We'll teach the key concepts behind time series analysis, visualization, and forecasting and how Pinot and Superset support these workflows.

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    Video

    Building a Run Simulator with Pinot and Streamlit

    In this talk, we will give a quick overview of Apache Pinot and dig a little into what makes it so fast. We will follow up by showing how to build a real-time analytics dashboard for a run simulator.

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    Video

    Building a real-time analytics dashboard with Streamlit, Apache Pinot, and Apache Kafka

    In this session we will learn how to build our own real-time analytics application on top of a streaming data source using Apache Kafka, Apache Pinot, and Streamlit.

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    Log Analytics

    External Article

    Pinot in YouGov

    Learn why YouGov chose Apache Pinot and how they migrated it into their node group.

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    External Article

    Ingesting OSQuery Logs To Apache Pinot

    In this blog we will feed the cleansed logs into Apache Pinot, a real-time OLAP database.

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    AdTech

    External Article

    Real-Time Exactly-Once Ad Event Processing with Apache Flink, Kafka, and Pinot

    This article focuses on how we leveraged open source technology to build Uber’s first “near real-time” exactly-once events processing system.

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    Blog

    Apache Pinot & Real-Time Ad Analytics By UberEats

    Here’s one example of how UberEats relied on Apache Pinot (along with other open sources technologies) to deliver accurate, real-time ad analytics at scale, and reliably.

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    External Article

    Apache Pinot Uncorks Real-Time Data for Ad-Tech Firm

    Sovrn uses Apache Pinot to bring data freshness from hours to seconds.

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