Event Messaging and Streaming with Apache Pulsar

What if you could scale messaging compute and storage independently? Learn how Apache Pulsar's architecture eliminates rebalancing issues for true cloud-native elasticity.

#1about 12 minutes

Understanding the fundamentals of event-driven systems

Key terminology in event computing is defined, including events, streams, event-driven architecture, and event sourcing.

#2about 5 minutes

Comparing event-driven and message-driven communication

The core differences between event-driven (pub/sub) and message-driven (queuing) messaging models are explained.

#3about 4 minutes

Why modern applications adopt event streaming

Event streaming enables real-time data processing for AI/ML and scalable cloud-native applications, contrasting with traditional batch processing.

#4about 11 minutes

An architectural overview of Apache Pulsar

Apache Pulsar is introduced as a cloud-native, multi-tenant platform that separates compute (brokers) from storage (Apache BookKeeper).

#5about 3 minutes

Exploring the unique features of Apache Pulsar

Pulsar's key advantages are highlighted, including its separation of compute and storage, built-in geo-replication, and flexible subscription models.

#6about 4 minutes

Building data pipelines with Pulsar Functions and IO

Pulsar Functions provide a lightweight, serverless framework for transforming data streams, complemented by Pulsar Schema and IO connectors.

#7about 6 minutes

Deploying Pulsar with the DataStax Astra platform

A demonstration shows how to use DataStax Astra Streaming, a managed cloud platform for Apache Pulsar, to create and manage streaming tenants.

#8about 8 minutes

Q&A on access control and the Java community

Questions are answered regarding managing access control in event-driven systems and the importance of open-source communities like the Java ecosystem.