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WeAreDevelopers LIVE β€’ Apr 18, 2023

Practical Change Data Streaming Use Cases With Debezium And Quarkus

Alex Soto

Eliminate the dual writes problem in your microservices. Learn to implement the outbox pattern with Debezium, Kafka, and Quarkus for scalable, fault-tolerant data consistency.

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#1 about 3 min

Session agenda for practical change data streaming

An introduction to overcoming synchronization inconsistencies using change data capture and messaging platforms.

#2 about 4 min

Understanding the dual rights anti-pattern in microservices

How propagating data to multiple services simultaneously leads to transaction failures and inconsistencies.

#3 about 6 min

Processing real-time event streams with Apache Kafka

Core concepts of a publish-subscribe system built for distributed fault-tolerance and scalability.

#4 about 4 min

Defining the change data capture architecture pattern

Capturing database insertions and updates to automatically populate a messaging topic for downstream consumptions.

#5 about 7 min

Extracting transaction logs using Debezium source connectors

Using connectors to monitor database transaction logs and emit standardized json event records.

#6 about 6 min

Implementing the transactional outbox pattern for reliable delivery

Writing domain events to a dedicated database table within a single transaction scope.

#7 about 5 min

Evolving legacy monoliths using the strangler fig pattern

A gradual migration strategy that mirrors real-time database changes from monoliths to new microservices.

#8 about 3 min

Deploying enterprise Java systems on Kubernetes with Strimzi

Configuring cluster operators and developing native cloud binaries to capture data changes.

#9 about 7 min

Demonstrating a practical outbox pipeline via Docker Compose

An interactive walkthrough showing how service requests trigger database logs that automatically populate topics.

#10 about 10 min

Answering architecture questions on downtime and custom messaging

Addressing challenges with manual synchronization, transaction failures before persistence, system design tools, and career growth.

Matching moments

6:56 min

Demonstrating event-driven auto-scaling with native Quarkus and Kafka

Daniel Oh Daniel Oh +1 Β· World Congress 2024

2:00 min

Moving from traditional databases to decoupled event streaming

Gerard Klijs Β· LIVE

4:30 min

Introducing data management and the shift to streaming

Mary Grygleski Mary Grygleski Β· LIVE

1:18 min

Technology stack for building robust microservices applications

Niklas Heidloff Β· LIVE

1:13 min

Overcoming integration challenges with Kafka Streams microservices

Denis Washington +1 Β· World Congress 2021

1:49 min

Integrating legacy monoliths with change data capture

David Leitner Β· World Congress 2022

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