IN PERSON! Apache Kafka® Meetup Singapore - July 2024

Hello everyone! Join us for an IN PERSON Apache Kafka® meetup on July 25th from 6:30pm.

📍 Venue:
WeWork Suntec Tower 5
5 Temasek Boulevard, 17th Floor Singapore, 038985

Room 17G

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🗓 Agenda:

• 18:30pm-19:00pm: Food, Drinks 🍻🥤 & Networking
• 19:00pm-19:40pm: Yining Liu, Software Engineer Intern, Risingwave Labs
• 19:40pm-20:20pm: Naveen Nandan, Staff Solutions Engineer, Confluent
• 20:20pm-20:30pm: Additional Networking

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💡 Speaker:
Yining Liu, Software Engineer Intern, Risingwave Labs

Talk:
Power of real-time media stream processing using Kafka and RisingWave

Abstract:
In this demo, we'll walk through the following steps:

1. Ingest Traffic Cam Videos into Kafka Topic: We'll start by streaming traffic camera videos directly into a Kafka topic, one topic per camera. This setup allows us to handle large volumes of video data in real-time.
2. Extract Car Plates Using RisingWave: Next, we'll leverage RisingWave to ingest each video frame from Kafka topic. Using a custom user-defined function (UDF), RisingWave will extract car plate numbers from the frames. This step demonstrates how RisingWave can perform complex, real-time data processing tasks.
3. Build a Real-Time Stream Job in SQL: We'll then create a real-time stream job using SQL to record the extracted car plates into a database. Additionally, we'll perform a batch query to trace a car's movements, showing which cameras captured the car and the corresponding timestamp.
4. Compute Resource Comparison (If Time Permits): Finally, if time allows, we'll compare the compute resource consumption between our system and a traditional re-identification (re-id) model. This comparison will highlight how a streaming database like RisingWave can significantly reduce overall compute requirements, with the help of Kafka, offering a more efficient solution for real-time data processing.

Bio:
Yining is a Software Engineer Intern at RisingWave Labs and a Computer Science/Statistics student at the University of Florida. He has experience with ML models and web development and is now venturing into the data engineering field. At RisingWave Labs, Yining is involved in building robust data solutions, driven by his interest in real-time streaming technologies.

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💡 Speaker:
Naveen Nandan, Staff Solutions Engineer, Confluent

Talk:
Building a Dynamic Pricing Engine using Stream Processing

Abstract:
Travel - Tickets/Hotel Prices, Commodities Market, Stock Trading, Sports Betting, and several other industries rely on various static and dynamic variables that determine the prices that the involved parties (Buyer/Seller) agree upon and make a transaction. In this talk let's explore how a dynamic pricing engine can be built using Kafka and other Stream Processing tools.

Bio:
Naveen is a Solutions Engineer with Confluent and works with customers and partners to help design, architect and implement event-streaming systems. Previous experience includes various aspects of systems architecture. Outside of work he is interested in sports, food and movies.

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DISCLAIMER
BY ATTENDING THIS EVENT IN PERSON, you acknowledge that risk includes possible exposure to and illness from infectious diseases including COVID-19, and accept responsibility for this, if it occurs.
As the classroom is a mask-on setting, please be reminded that masks should still be worn at all times unless actively eating or drinking
NOTE: We are unable to cater for any attendees under the age of 18.

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If you would like to speak or host our next event please let us know! community@confluent.io