What is the internet of things (IoT), and how does it relate to event streaming and Apache Kafka®? The deployment of Kafka outside the datacenter creates many new possibilities for processing data in motion and building new business cases.

In this episode, Kai Waehner, field CTO and global technology advisor at Confluent, discusses the intersection of edge data infrastructure, IoT, and cloud services for Kafka. He also details how businesses get into the sticky situation of not accounting for solutions when data is running dangerously close to the edge. Air-gapped environments and strong security requirements are the norm in many edge deployments.

Defining the edge for your industry depends on what sector you’re in plus the amount of data and interaction involved with your customers. The edge could lie on various points of the spectrum and carry various meanings to various people. Before you can deploy Kafka to the edge, you must first define where that edge is as it relates to your connectivity needs. 

Edge resiliency enables your enterprise to not only control your datacenter with ease but also preserve the data without privacy risks or data leaks. If a business does not have the personnel to handle these big IT jobs on their own or an organization simply does not have an IT department at all, this is where Kafka solutions can come in to fill the gap. 

This podcast explores use cases and architectures at the edge (i.e., outside the datacenter) across industries, including manufacturing, energy, retail, restaurants, and banks. The trade-offs of edge deployments are compared to a hybrid integration with Confluent Cloud. 

EPISODE LINKS

• Processing IoT Data End to End with MQTT & Kafka
• End-to-End Integration: IoT Edge to Confluent
• Infrastructure Checklist for Kafka at the Edge
• Use Cases & Architectures for Kafka at the Edge
• Architecture Patterns for Distributed, Hybrid, Edge & Global Kafka Deployments
• Building a Smart Factory with Kafka & 5G Campus Networks
• Kafka Is the New Black at the Edge in Industrial IoT, Logistics & Retailing 
• Kafka, KSQL & Apache PLC4X for IIoT Data Integration & Processing 
• Streaming Machine Learning at Scale from 100K IoT Devices with HiveMQ, Kafka & TensorFlow 
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