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Data Streaming with Event Hubs
for Kafka
Dinusha Kumarasiri
About Me
 Microsoft MVP for Microsoft Azure
 Microsoft Certified Trainer (MCT)
 Senior Consultant at Dialog Information Technology
 Cloud Enthusiast
 Love to share what I learn
Agenda
 Event Driven Architecture
 Event Streaming
 Event Streaming with Apache Kafka
 Event Streaming with Azure Event Hubs
 Event Hubs for Kafka
 Demo
Event Driven Architecture
 Traditional API Driven
Problems
 Dependency among APIs
 Difficult to Scale each component
 Lack of Resiliency
Motivation
 Easy to build for simple
operations
Event Driven Architecture
 Event Driven
Motivation
 Components are Loosely Coupled
 Components communicate through Events
 Can Scale individually
 Improved Resiliency
Event Streaming
 Continuous Flow of information
 Events are meaningful when it composes an event Stream
 Event processing can combine multiple event streams
Producer 1
Producer 2
Producer 3
Event Stream
Platform
Consumer 1
Consumer 2
Consumer 3
Fraud Detection
Predictive
Maintenance
Real-Time
Monitoring
Supply Chain
Optimization
Customer
Experience
Event Streaming with Apache Kafka
 Open Source
 Can build Kafka cluster with multiple nodes (Brokers)
 Hold a stream of events in Topics, which can be built with multiple Partitions
 Fully managed Kafka cluster can be built with Apache Kafka for Confluent Cloud
Event Streaming with Azure Event Hub
 Cloud Native offering for Event Streaming in Microsoft Azure
 Designed for High-volume event Ingestion and Processing
 Scalable and Reliable platform
 Data automatically partitioned and distributed across multiple instances
Azure Event Hubs for Apache Kafka
 Fully managed Kafka-Compatible service provided by Microsoft Azure
 Allows Kafka APIs and Tools to produce & consume data with Event Hubs
 Enables the migration of Kafka applications to Azure
Demo https://github.com/Azure/azure-event-hubs-for-kafka
Event Streaming – Big Picture
Event Producers Event Ingestion Stream Processing Insights
dinushaonline.blogspot.com @kumarasiri048 dinushak Dinusha Kumarasiri

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Azure Event Hubs for Kafka.pptx

  • 1. Data Streaming with Event Hubs for Kafka Dinusha Kumarasiri
  • 2. About Me  Microsoft MVP for Microsoft Azure  Microsoft Certified Trainer (MCT)  Senior Consultant at Dialog Information Technology  Cloud Enthusiast  Love to share what I learn
  • 3. Agenda  Event Driven Architecture  Event Streaming  Event Streaming with Apache Kafka  Event Streaming with Azure Event Hubs  Event Hubs for Kafka  Demo
  • 4. Event Driven Architecture  Traditional API Driven Problems  Dependency among APIs  Difficult to Scale each component  Lack of Resiliency Motivation  Easy to build for simple operations
  • 5. Event Driven Architecture  Event Driven Motivation  Components are Loosely Coupled  Components communicate through Events  Can Scale individually  Improved Resiliency
  • 6. Event Streaming  Continuous Flow of information  Events are meaningful when it composes an event Stream  Event processing can combine multiple event streams Producer 1 Producer 2 Producer 3 Event Stream Platform Consumer 1 Consumer 2 Consumer 3 Fraud Detection Predictive Maintenance Real-Time Monitoring Supply Chain Optimization Customer Experience
  • 7. Event Streaming with Apache Kafka  Open Source  Can build Kafka cluster with multiple nodes (Brokers)  Hold a stream of events in Topics, which can be built with multiple Partitions  Fully managed Kafka cluster can be built with Apache Kafka for Confluent Cloud
  • 8. Event Streaming with Azure Event Hub  Cloud Native offering for Event Streaming in Microsoft Azure  Designed for High-volume event Ingestion and Processing  Scalable and Reliable platform  Data automatically partitioned and distributed across multiple instances
  • 9. Azure Event Hubs for Apache Kafka  Fully managed Kafka-Compatible service provided by Microsoft Azure  Allows Kafka APIs and Tools to produce & consume data with Event Hubs  Enables the migration of Kafka applications to Azure
  • 11. Event Streaming – Big Picture Event Producers Event Ingestion Stream Processing Insights