Apache Pinot Meetup at Uber, Palo Alto

Using Apache Pinot at Scale: Experiences from Uber, LinkedIn, & Other Companies

Apache Pinot is a distributed columnar database for real-time analytics. It powers many well-known applications, including LinkedIn’s Who Viewed My Profile feature and Uber Eats timely analytics. Since Pinot joined Apache Incubator in 2019, it has gained a lot of momentum with a growing community. We will be announcing the upcoming release of Pinot which introduces numerous major features and underwent extensive refactoring to make Pinot easier to use. Join us to learn about all the cool things that went into the new version of Pinot!

Agenda
6:00-6:30 pm Door opens, networking
6:30-8:00 pm Talks
8:00-8:30 pm Happy hour

Talks:

1. History of Pinot - Kishore Gopalakrishna (startup)

Two of the most frequently asked questions about Pinot’s history are “Why did LinkedIn build Pinot?”, “How is it different from Druid, ElasticSearch, Kylin?”. In this talk, we will go over the use cases that motivated us to build Pinot and how it has changed the analytics landscape at LinkedIn, Uber, and other companies.

2. Leveraging Pinot at Uber for Large-scale Analytics - Haibo Wang and James Shao (Uber)

In this presentation, we will discuss how Pinot empowers timely analytics at Uber. We will also dive into how we built an open-source Presto connector to enable complex SQL queries on Pinot and other data sources, and how we developed upsert into Pinot to allow mutable data in real-time tables.

3. New Features on Pinot: standard SQL, cloud readiness and more - Siddharth Teotia (LinkedIn)

In this presentation, we will talk about new features -- ORDER BY, DISTINCT, Text Search, the road to Standard SQL, etc.. We will also go over the work being done to get Pinot cloud-ready, and briefly discuss the roadmap and plans for graduation.

4. Pinot stack on Kubernetes: deployment and integrations - Xiang Fu (startup)

In this presentation, we will talk about how to deploy Pinot on Kubernetes as well as how to integrate with Superset for visualization and Presto for ad hoc exploration.