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IndiQube - Grape Garden, SURVEY NO.130, 18TH MAIN, 1ST A CROSS, 6TH BLOCK, KORAMANGALA, BENGALURU – 560095
| Name | Speaker | Start Time | End Time | Presentation | Recording |
|---|---|---|---|---|---|
| Welcome and registration | 10:00 AM | 10:20 AM | |||
| Pinot , Why are you so fast | Jayesh Asrani, StarTree | 10:20 AM | 11:00 AM | Slides | |
| Advanced analytics with Flink and Kafka | Vijay Narayanan, Confluent | 11:00 AM | 11:40 AM | ||
| Networking break | 11:40 AM | 12:00 PM | |||
| hi-performance data pipelines with Rust+Haskell+Kafka | Paul Victor, JusPay | 12:00 PM | 12:40 PM | Slides | |
| Building Real time ML with Kafka and Flink | Ashwin Venkatesan and Avinash Upadhyaya, Platformatory.io | 12:40 PM | 01:20 PM | Slides | |
| Lunch and Networking | 01:30 PM | 2:30 PM |
Speaker: Vijay Narayanan, Staff Solutions Engineer at Confluent
About the talk: This session will start with an overview of Flink and how Flick and Kafka come together to provide stream processing. We will dig into some interesting and unique capabilities in Flink and end with a short demo.
Speaker: Paul Victor, Senior Architect at Juspay
About the talk: Juspay’s data platform handles 5 million transactions/day and close to 1 billion events from various microservices both on the customer device SDK and application servers. The stream of events are used to power realtime analytics to merchants while optimizing on metrics like performance(which indirectly translates to cloud costs), accuracy and realtimeness. We chose haskell and rust as the language of choice to run the backbone of our data processing application. The raw CPU performance of these data processing applications is tremendous and has helped us reduce our cloud costs. There are challenges in architecting such applications as we steered clear of traditional data processing frameworks, which I will briefly talk about.
Speaker: Jayesh Asrani, Solutions Architect at StarTree
About the talk: Apache Pinot Engine 101 , Query Processing , optimising techniques on what makes Pinot fast with a peak under the hood of the Apache Pinot engine and what makes it lightning fast.
Speaker: Ashwin Venkatesan & Avinash Upadhyaya, Platform Engineers at Platformatory
About the talk: In this talk, we will explore the idea of building real time machine learning systems with Apache Kafka and Apache Flink. The objective is to demonstrate feature pipelines that compute these real-time features (using Flink) and copy (using connectors) to a feature store a model serving layer (HTTP) that picks up real-time features and provides them as inputs to the model model that delivers real-time prediction
