Best Practices for Streaming Connected Car Data With MQTT.fx and Kafka
Watch the Webinar
Chapters
- 10:19 - A Streaming Platform is the Underpinning of an Event-driven Architecture
- 11:24 - Kafka Trade-offs (from lot perspective)
- 12:31 - (De facto) Standards for Processing lot Data
- 21:44 - Start Small, but Prepare for Scalability from Beginning
- 24:45 - Comparison with other MQTT options
- 26:07 - HiveMQ Kafka Advantage
- 28:16 - Hive MQ Kafka Solution
- 30:51 - Choose the Right Tool Stack and Infrastructure
- 31:56 - Separation of Concerns
- 34:18 - Different Data for Different Use Cases
- 36:53 - Demo 100.000 Connected Cars
- 38:17 - HiveMQ Enterprise Extension for Kafka
- 38:52 - The HiveMQ Platform - Open Source and Enterprise-grade
- 40:23 - Introducing Confluent Platform
- 42:39 - Our Customers Are...
- 44:00 - Spend your time on your applications!
- 44:36 - Next Steps...
Webinar Overview
Automotive organizations today are looking to stream Internet of Things (IoT) data generated from connected cars to Apache Kafka. However, connecting thousands or even millions of cars over unreliable networks can create architectural challenges.
In this session, Florian Raschbichler, Head of Support at HiveMQ, identifies and demonstrates the best practices for implementing a large-scale connected car platform that can stream MQTT messages to Kafka.
Key topics and takeaways:
Why MQTT is the de-facto communications protocol for a scalable & reliable connected car platform
Why Kafka is the most used data streaming and processing protocol in the enterprise backend
How MQTT and Kafka work perfectly together to establish reliable IoT data streaming and processing
Florian Raschbichler
Florian serves as the head of the HiveMQ support team with years of first hand experience overcoming challenges in achieving reliable, scalable, and secure IoT messaging for enterprise customers.
