Making sense of Event-Driven Systems @ Oracle Code One 2019

Presented at Oracle Code One 2019
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Making sense of Event-Driven Systems @ Kafka Summit 2019

Presented at Kafka Summit NYC 2019
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The Importance of Distributed Tracing for Apache Kafka Based Applications

Originally posted in Confluent Blog Apache Kafka® based applications stand out for their ability to decouple producers and consumers using an event log as an intermediate layer. One result of this is that producers and consumers don’t know about each other, as there is no direct communication between them. This enables choreographed service collaborations, where many components can subscribe to events stored in the event log and react to them asynchronously.
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The Importance of Observability for Kafka-based applications with Zipkin @ Oslo Apache Kafka Meetup

Presented at Oslo Apache Kafka Meetup
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Distributed Tracing with OpenTracing @ NoSlidesConf 2017

Presented at NoSlidesConf 2017
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Tracing Kafka applications

for a more updated version, check https://jeqo.github.io/posts/2019-03-26-importance-of-distributed-tracing-for-apache-kafka-based-applications/ Tracing is one of the hardest time in integration or microservice development: knowing how a request impact your different components, and if your components have behave as expected. This could be fairly easy if we have monolith where we have one database and with some queries or checking one log file you can validate everything went well. Once you introduce distributed components and asynchronous communication this starts to get more complex and tedious.
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From Messaging to Logs with Apache Kafka @ OUGN 2017

Presented at OUGN 2017
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Scaling WebLogic, the Kubernetes way @ OUGN 2017

Presented at OUGN 2017
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Rewind Kafka Consumer Offsets

One of the most important features from Apache Kafka is how it manages Multiple Consumers. Each consumer group has a current offset, that determine at what point in a topic this consumer group has consume messages. So, each consumer group can manage its offset independently, by partition. This offers the possibility to rollback in time and reprocess messages from the beginning of a topic and regenerate the current status of the system. But how to do it (programmatically)?
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Scaling Kafka with Docker Containers

In this post I will show how to use Docker containers to create and scale a Kafka cluster, and also how to create, scale and move topics inside the cluster.
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