Apache Kylin: Achieve Exact COUNT DISTINCT with Sub-Second Latency at PB Scale

Virtual Session: Please see zoom info for joining below

To kick things off, Chloe Kwon and Kevin Horecka will be telling you briefly, but enthusiastically, about Data in the News! What models, discoveries, and tools were in the news during the month of April and May? Come to the event and find out!

Then, for our main event, we'll hear from Kaige. Kaige is a senior solutions architect at Kyligence, where he works on building the next-generation big data analytics platform. Previously, he worked on the OpenStack and Bluemix team at IBM, focusing on cloud computing and virtualization technology. Kaige loves the open source community and is an active Apache Kylin committer.

With over 450 million customers, Didi (world’s largest rideshare company) conducts complex user behavior analysis on huge datasets daily. Exact Count Distinct is one of Didi’s most critical metrics, but it is known for being computationally heavy and notoriously slow. The difference between exact Count Distinct and approximate Count Distinct can cost Didi millions of dollars. In this talk, Kaige Liu of the Apache Kylin project will explain how Didi uses Apache Kylin to return exact Distinct Count on billions of rows of data with sub-second latency to generate the most accurate picture of its business.

You will also learn about the latest development in modern OLAP technologies. Kaige will share how Didi and Truck Alliance (a truck-hailing company that processes $100 billion worth of goods yearly) use Apache Kylin to power their analytics platforms that allow 100s of analysts to achieve sub-second latency on petabyte-scale data.

We welcome inputs from interested Data Science enthusiasts and professionals on these ideas, techniques, and their implementation as well as any other questions or comments. Our goal is to make this event fun and informative!

Agenda:

7:00 - 7:15 - Data in the News
7:15 - 8:00 - Talk + Q&A
8:00 - 8:30 - Discussion !

The event is free, and everyone is welcome.

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Meeting ID: 331 026 1107