Data Storage, OLAP

Apache Pinot Series Summary: Real-Time Analytics for Modern Business Needs

by Kinshuk Dutta
This entry is part 1 of 6 in the series Pinot Series
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Pinot Series

Over the past few months, we’ve explored the capabilities of Apache Pinot as a powerful real-time analytics engine. From basic setup to advanced configurations, this series has covered the essential steps to building robust, low-latency analytics solutions. Below is a summary of each blog post in the series, along with some real-world use cases demonstrating how companies use Pinot to address critical business challenges.

Series Overview and Links

Here’s a quick recap of the posts in this series, with links and publication dates:

  1. Pinot™ Basics
    Published: February 27, 2021
    Introduction to Apache Pinot’s core features and initial setup, with guidance on using Pinot for basic analytics use cases.
  2. Advanced Apache Pinot: Sample Project and Industry Use Cases
    Published: November 16, 2023
    Building a sample project for real-time analytics, with industry scenarios and a step-by-step guide to configuring Pinot with a Kafka-powered data ingestion pipeline.
  3. Production-Ready Apache Pinot: Deployment and Integration with Iceberg
    Published: November 30, 2023
    Transitioning to a production deployment, leveraging Iceberg for long-term storage, and handling complex data retention and security needs.
  4. Advanced Apache Pinot: Custom Aggregations, Transformations, and Real-Time Enrichment
    Published: December 28, 2023
    Finalizing the project with advanced aggregations, custom transformations, and real-time data enrichment to support deeper analytics and faster querying.

Each post builds on the previous one, offering a roadmap for using Apache Pinot in real-world, production-grade scenarios.

Real-Life Business Use Cases of Apache Pinot

Many organizations rely on Apache Pinot to power real-time analytics applications. Here are a few standout examples, along with links to the stories shared by the companies themselves:

1. LinkedIn: Real-Time Insights into User Activity

LinkedIn originally developed Pinot to provide real-time analytics on user interactions, powering features like Who Viewed My Profile and Job Recommendations. Pinot’s low-latency query capabilities allow LinkedIn to offer up-to-the-second insights into user engagement on a massive scale.

2. Uber: Real-Time Monitoring and Decision-Making

Uber uses Apache Pinot to power real-time monitoring dashboards, helping the team stay informed of system health and performance metrics across its global operations. Pinot allows Uber to handle millions of events per second, ensuring the company’s monitoring systems are both fast and scalable.

3. Stripe: Financial Reporting and Transaction Insights

Stripe relies on Pinot to provide real-time insights into transaction data, enabling businesses to monitor their financial health with up-to-the-minute accuracy. With Pinot, Stripe can generate complex reports on massive data sets with minimal latency, allowing customers to make informed business decisions.

4. Walmart: Enhancing the E-Commerce Experience

Walmart uses Pinot to analyze customer behavior on its e-commerce platform in real-time, ensuring product recommendations and search results are always relevant. This has improved customer experience by delivering tailored recommendations based on customer interaction data as it happens.

5. WePay: Fraud Detection and Transaction Monitoring

WePay leverages Pinot to monitor transactions in real time, helping detect and prevent fraud. Pinot’s low-latency capabilities enable WePay to analyze patterns as they emerge, offering a vital layer of protection against financial fraud.

These examples illustrate the versatility and robustness of Apache Pinot across industries, from e-commerce and finance to technology and transportation.

Conclusion and What’s Next

As outlined in the Druid summary blog, our next post will provide a comprehensive comparison between Apache Druid and Apache Pinot. Both tools are highly effective for real-time analytics, yet each offers distinct advantages and trade-offs. This upcoming blog will explore:

  • Strengths and Weaknesses of Druid and Pinot
  • When to Choose One Over the Other, based on data needs and query types
  • Specific Scenarios where each shines in real-world applications

Stay tuned as we delve into Druid vs. Pinot, offering a practical guide on when to use each for optimal performance in analytics workloads.

Thank you for joining us on this journey through Apache Pinot, and we look forward to diving deeper into real-time analytics in the next comparison post!

Series NavigationAdvanced Apache Pinot: Custom Aggregations, Transformations, and Real-Time Enrichment >>