Karina Varela works with enterprise technologies for over ten years, and for the past years with focus on cloud-native technologies. She's currently part of the App. Services BU at Red Hat, and takes care technically of several products and managed services.
Her solid knowledge is built on field experience with development, architecture, delivery, and troubleshooting of customer's applications and enterprise cloud solutions used around the world. Karina became well recognized amongst the Java community for being an international speaker, writer, open-source contributor and manager of the biggest Java community of the world, SOUJava.
To check her content and get know her better, go to kvarela.me.
The persistence mechanism is the heart of most applications and microservices, although architects often happen not to give as much attention to such components. A wrong choice and design, will impact the whole system's functioning, no matter how much you scale up the service. Join this session and learn how to avoid this from happening.
In this guided lab, you'll learn tips and tricks on creating a healthier app with an increased performance by improving its relation with database persistence.
From a Java perspective, you'll try not only more conventional options like SQL and NoSQL but also some trendy new solutions in the market like MicroStream. You'll also have hands-on experience with this open-source ultrafast in-memory data persistence storage allows queries to be up to 1000x times faster than traditional relational databases.
Think of that one legacy service in production. Well, you've just been asked to add new functionalities to it. You can already predict the pain in this modernization:
- code changes are ineffective and error-prone, and rewriting may ask for insane amounts of work;
- you can't move it to cloud environments due to high predictable costs;
- if creating new services, you must overcome the challenges of handling data in distributed systems (e.g., dual-write, eventual consistency, distributed transactions)
Luckily, you can join this talk and see the way out!
In this session, we'll explore a modernization strategy with the outbox pattern. Event-driven technologies and cloud adoption boosts this architecture's solution through enterprise data integration patterns - consequently opening new possibilities for your use cases deliveries.
Let's run through an actual implementation to learn how change data capture (CDC) allows extending apps by immersing them - with zero code changes - within a cloud-based event-driven microservice architecture. This CDC on steroids relies on cloud-native technologies like Kafka, Kafka Streams, Quarkus, Camel, and OpenShift.
Best of all: You can leave this session with extra architectural knowledge, a complete architectural technical guide, a step-by-step guide to trying the solution and getting your hands dirty, and finally, feeling confident to apply it in your context when needed.
It's time to gear up and extend your existing tech stack!