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.
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