Tech Week Meetup:
Data in Motion NYC: Messaging, Streaming & Processing
Experience an afternoon of high-level networking and technical insights over a refreshing summer lunch. Discover how to effectively scale your data pipelines with Apache Beam and optimize your messaging integration using Apache ActiveMQ alongside industry peers.
đź“ť Agenda
11:00 AM – 11:20 AM: Arrival & Networking
11:20 AM – 12:00 PM: Data in Motion with Apache ActiveMQ and Apache Beam | JB Onofré, Principal Software Engineer, Dremio + Director, Apache Foundation
12:00 PM – 1:00 PM: Lunch & Networking (on the rooftop if the weather permits)
1:00 PM – 1:40 PM: GraphFlow & Beam: Pythonic, Scalable GNN Pipelines | Yogesh Tewari, Senior Cloud Data Engineer, Google
1:40 PM – 2:00 PM: Final Networking
đź’ Talks
Data in Motion with Apache ActiveMQ and Apache Beam By: JB Onofré, Principal Software Engineer, Dremio + Director, Apache Foundation.
Modern data architectures demand more than batch processing — they require reliable, scalable, and flexible pipelines that can handle data as it moves. This session explores the powerful combination of Apache ActiveMQ, a battle-tested message broker for enterprise messaging, and Apache Beam, a unified programming model for both batch and streaming data processing.
GraphFlow & Beam: Pythonic, Scalable GNN Pipelines By: Yogesh Tewari, Senior Cloud Data Engineer at Google.
Learn how GraphFlow, a modular Python toolkit, utilizes Apache Beam to create efficient and scalable data pipelines for Graph Neural Networks (GNNs). We’ll demonstrate how GraphFlow on Beam tackles large-scale graph data challenges, including distributed ingestion from cloud databases, scalable feature normalization, graph sampling, and online model inference.
