Please park in our underground garage (entrance on Sycamore just south of Hollywood Blvd) and be sure to bring your ticket in with you so that we validate it for you.Take the elevator up to the lobby (“L”)
- Check In will be in the Lobby Area -
Join us for our inaugural Apache Kafka meetup on May, 22nd, hosted by Ticketmaster in LA. The address is 1st Floor Forum Theater, 7060 Hollywood Blvd, 90028 . The agenda and speaker information can be found below. See you there!
6:30pm: Doors open
6:30pm - 6:45pm: Networking, Pizza and Drinks
6:45pm - 7:15pm: Presentation #1: Apache Kafka and The Rise of Real-Time, Neha Narkhede, Confluent
7:15pm - 7:45pm: Presentation #2: Doing it live: machine learning at scale... and in an instant, Chris Smith, Ticketmaster
7:45pm - 8:15pm: Additional Q&A and Networking
Neha Narkhede is co-founder and CTO at Confluent, a company backing the popular Apache Kafka messaging system. Prior to founding Confluent, Neha led streams infrastructure at LinkedIn, where she was responsible for LinkedIn’s streaming infrastructure built on top of Apache Kafka and Apache Samza. She is one of the initial authors of Apache Kafka and a committer and PMC member on the project.
Apache Kafka and The Rise of Real-Time
What happens if you take everything that is happening in your company—every click, every database change, every application log—and make it all available as a real-time stream of well-structured data?
Neha will discuss the experience at LinkedIn and elsewhere moving from batch-oriented ETL to real-time streams using Apache Kafka, including how the design and implementation of Kafka was driven by the goal of acting as a real-time platform for event data as well as some of the challenges of scaling Kafka to hundreds of billions of events per day at Linkedin, supporting thousands of engineers, applications and data systems in a self-service fashion.
She will describe how real-time streams can become the source of ETL into Hadoop or a relational data warehouse, how real-time data can supplement the role of batch-oriented analytics in Hadoop or a traditional data warehouse, and how applications and stream processing systems such as Storm, Spark, or Samza can make use of these feeds for sophisticated real-time data processing as events occur.
VP Data Sciences, Ticketmaster
Doing it live: machine learning at scale... and in an instant
Ticketmaster operates in a very real-time environment, where we can go from tickets not being for sale, to being on sale, to being held for someone to purchase, to an entire event being sold out in minutes. It's a diverse environment, with well over a dozen ticketing systems, creating challenges for observing and reacting wholistically. While operations are typically consistent from one event to the next, the dynamic & evolving market means that what was the correct market decision in one case, might not be the correct decision in the next. With sales sometimes over in minutes, waiting even an hour to react to changes in woefully inadequate. Consequently, in order to apply machine learning, we need a system & infrastructure that can consider data as it is produced from a diverse set of systems and learn new decisioning logic as it receives each new piece of data.
This talk will describe how Ticketmaster has combined open source tools like 0mq, Kafka, Storm, Vowpal Wabbit, etc. that allows millisecond reactions to a highly dynamic ecosystem and market place, allowing systems to learn in real-time to respond to & learn from market conditions that might not have existed moments before. It will also discuss how we leverage data tools like CKAN to provide accurate and up to date catalog of data across a diverse data ecosystem.
Special thanks to Ticketmaster (http://www.ticketmaster.com/) who are hosting us for this event.
Don't forget to join our Community Slack Team (https://slackpass.io/confluentcommunity)!
If you would like to speak or host our next event please let us know! [masked]
NOTE: We are unable to cater for any attendees under the age of 18. Please do not sign up for this event if you are under 18.